How Generative AI Is Helping Salesforce CRM

 Sales have never been easy; today, sales and service teams are loaded with tedious tasks. A sales rep might spend half the day drafting emails and logging data, and only a few hours selling. A support agent might hunt through a dozen systems to answer a simple customer question. This is incredibly inefficient. Salesforce reports that only about 28% of sellers expect to hit their quotas — a sign that too much time is wasted on data recording and reporting. Marketing teams face similar struggles, cranking out copy and reports instead of focusing on strategy. The result? Missed opportunities, unhappy customers, and exhausted employees.

The pain is real. When teams are tied in routine work, deals slip away, and customers grow frustrated. Without help, employees spend hours on low-value tasks, leaving little room for creativity or relationship-building. A Salesforce survey found that 71% of marketers believe generative AI could “eliminate busy work,” saving them roughly five hours per week. In other words, they feel crushed by manual chores and see AI as the only way out. Without that extra help, companies risk falling behind more agile competitors.

Fortunately, a new kind of assistant is on the scene. Salesforce has integrated generative AI — the same smart tech behind ChatGPT — directly into its CRM products. Now, your CRM can create content and insights on the fly. Think of it as an AI co-pilot that lives inside Salesforce. It knows your customers and data, and it can draft messages, summarise information, and even suggest actions. For example, Salesforce says its Einstein GPT can “generate personalised emails for salespeople,” “specific responses for customer service professionals,” and “targeted content for marketers.” In practice, this means you could log into Salesforce and ask it to write a follow-up email to a client, and it will produce a customised draft in seconds. This isn’t a generic answer from the internet; it’s built on your own CRM data and company knowledge.

Generative AI acts like a smart assistant built into Salesforce. Instead of writing everything from scratch, you tell it what you need and it writes a first draft for you, all based on real customer data. This frees employees to focus on strategy and relationships rather than busywork. Salesforce calls Einstein GPT “the world’s first generative AI for CRM,” combining its own AI with advanced language models to make every employee more productive.

Generative AI for Sales Teams

Sales reps are experiencing significant relief from all of these tools. Salesforce’s latest Sales AI capabilities can auto-generate emails, schedule a meeting, and set next steps for you. Just say to your CRM, “Send an email about cross-selling product X to company A,” and a few seconds later, receive a ready-to-send draft with the correct tone and specifics. AI is also able to scan previous calls and notes, extracting important details, and it saves time and effort. For instance, Einstein AI Insights transcribes and summarises sales calls in a matter of moments, alerting you to customer objections, competitor names, or crucial pricing conversations. It even examines your pipeline: generative AI reviews open opportunities and other factors to determine which deals are likely to close. The result? You’re spending less time guessing which accounts to pursue and more time engaging high-potential buyers.

In reality, those AI assistants shift things. Salesforce finds that with Einstein GPT, reps are spending less time on data entry and more on actually selling, which equals faster outreach and more closed opportunities. For example, Einstein GPT leverages real-time data to create “highly personal, relevant prospect emails” so teams can spend less time typing and more time engaging with clients. It also qualifies leads automatically, so you only speak with the right contacts at the right time. In fact, industry analysts point out that companies using AI are already opening up 30% of employee time for value-added activities. Sales teams are free to use that extra time developing key relationships or coming up with winning plans instead of fighting with tasks that AI is designed to perform.

Intelligent Customer Service

Service teams also have an advantage. Support agents usually get repetitive queries and complicated case histories. Now, with generative AI, support agents are given an assistant. Einstein GPT for Service is capable of authoring knowledge base articles based on previous case notes and even “automatically generate personalised agent chat responses” to clients. In short, rather than beginning from scratch, an agent is provided with a customised reply within a matter of seconds and is able to modify it according to their needs. This accelerates assistance and makes responses consistent.

The numbers are already remarkable. Salesforce’s AI-based service tools reduced response times by a huge margin at Pentagon Federal Credit Union (PenFed). PenFed integrated its core banking infrastructure and member data into Salesforce and deployed AI-powered chatbots and virtual agents. Now, 60% of member queries go through those AI-based channels, so members receive instant answers on their own. Chats and bot interactions surged 223%, and first-call resolution increased by 20%. In a nutshell, customers receive answers sooner, and human agents handle tougher issues. PenFed’s experience demonstrates what’s possible with generative AI for transforming the support centre into a smooth-running, largely self-service operation.

Machine-Driven Marketing and Personalisation

Marketing teams are constantly being pushed to create content and campaigns — an ideal application for AI’s imaginative capabilities. Einstein GPT for Marketing can “dynamically generate personalised content to connect with customers” for email, social, web, and ads. Require three alternative subjects for a series of customer segments? AI will write them. Need a draft of a promotional text or advertisement copy? AI can do that too. Because it’s based on your Salesforce data, the tone and promotions can be customised based on actual customer interest. That’s a massive time-saver. Salesforce’s research established that 71% of marketers believe that generative AI will remove busywork for them. On average, they estimate it will save more than five hours a week — essentially a full month every year — allowing them to return to strategic and creative tasks.

Early indications confirm such optimism. Several businesses have conducted AI-based A/B tests and experienced improved engagement. And since Salesforce integrates all of this into Marketing Cloud, content generation is tied directly back into campaigns and analytics. You receive tailored emails or ads nearly instantly, then shape them based on real-time input. More simply put, marketing teams have an AI copywriter and strategist at their disposal, enabling them to deliver a message at the right time to the right individual.

Increasing Team Productivity

Generative AI is for more than just customers — it benefits internal teams, too. With Salesforce owning Slack, they also embedded AI into Slack chat. AI within Slack can summarise lengthy channel conversations, dig through previous messages for answers, and even pull relevant CRM information from Salesforce — simply by asking plain-English questions. This addresses that old “I missed that meeting, now I need to read 500 messages” challenge. Salesforce estimates that AI features within Slack save every user nearly 97 minutes every week. That’s about two additional workdays back in your team’s hands every month!

Real customers validate massive benefits. SpotOn, a retail software firm, reported to Salesforce that during a pilot of Slack AI “Slack AI has accelerated our employees’ work exponentially,” with “significant productivity benefits” for their staff. Anthropic, an AI firm, discovered that users of Slack AI were a “huge productivity increase” — staff could immediately find answers and work on high-value activities. Even within Salesforce CRM, new Copilot functionality allows a sales representative to request a personalised close plan or deal analysis within Sales Cloud, rather than sifting through reports. In all instances, there is a consistent theme: routine internal tasks are taken care of by AI so individuals can work smarter.

Case Study: PenFed Credit Union’s Success Story

PenFed’s experience is a prime demonstration of these advantages at work. As the second-largest US federal credit union, PenFed required quick service and secure data. They leveraged Salesforce’s Data Cloud and MuleSoft to unify customer data, then created AI chatbots within Financial Services Cloud. The chatbots serve up AI-driven responses based on actual account data. The result was staggering: 60% of member complaints are now self-service, courtesy of the chatbots, with total chatbot usage increasing 223%, and first-call resolution rising by 20%. In everyday terms, a member interested in moving money or checking a balance is immediately able to receive assistance from an AI assistant, as opposed to waiting around. This change benefited member satisfaction and also freed up PenFed’s staff to focus on more challenging cases and strategic pursuits.

Conclusion

Generative AI isn’t a magic wand — it depends on quality data and oversight. As the Salesforce AI leader said, “Data is fuel for AI — without high-quality, trusted data, it becomes ‘garbage in, garbage out.’” Many marketers remain cautious; 39% say they don’t know how to safely use generative AI, and many worry about accuracy. That’s why Salesforce includes safeguards like the Einstein Trust Layer and Data Cloud, ensuring AI outputs are based on secure, private data, not random web content. AI suggestions should be treated as first drafts, reviewed and refined before use. With strong data governance and training, the benefits outweigh the risks.

We’re entering a new era of CRM — moving from passive data systems to AI-enhanced platforms that assist creatively and analytically. Companies already use these tools to save time and boost engagement. The key: start with a secure, focused rollout (e.g., email drafts), then scale once results are clear. As CEO Marc Benioff puts it, every company will undergo an AI transformation. Generative AI in Salesforce empowers people — it helps sales, service, and marketing teams spend less time on busywork and more time delivering value.

AI-Powered Property Valuation & Investment Analysis in Real Estate

 

Introduction

 Artificial intelligence has impacted every aspect of humans and businesses, and will continue to do so. Here in this article, we will explore the impact of AI in real estate valuations. In a previous blog, we have already seen how AI is bringing in the change and impacting the real estate sector.  We will see how AI is impacting valuations, its benefits, current challenges, and the future potential of intelligent systems in enhancing decision-making and operational efficiency in the sector.

Traditionally, the real estate industry has depended on human expertise, historical market trends, and economic indicators for property valuation and investment analysis. Today, artificial intelligence (AI) is transforming these practices by introducing data-driven insights, automated processes, and advanced algorithms.

AI-powered property valuation leverages machine learning, predictive analytics, and big data to deliver highly accurate property assessments. Similarly, AI in investment analysis enables real estate professionals, investors, and homebuyers to process vast datasets, forecast market trends, and develop optimised
investment strategies with reduced risk.

Understanding AI-Powered Property Valuation

Property valuation plays a crucial role in real estate transactions, investment decisions, and tax assessments. While traditional methods depend on comparative market analysis (CMA), manual appraisals, and broad economic indicators, artificial intelligence (AI) is revolutionising the process with automation, precision, and predictive analytics.

AI-Driven Automated Valuation Models (AVMs)

Automated Valuation Models (AVMs) are digital tools that estimate real estate market value using mathematical algorithms and property databases. With their speed, cost-efficiency, and accessibility, AVMs have become essential for banks, real estate agents, investors, and homebuyers alike. Automated Valuation Models (AVMs) are at the forefront of this transformation. Powered by AI algorithms, AVMS analyse extensive datasets—including recent sales data, neighbourhood trends, infrastructure growth, and historical pricing—to deliver accurate, real-time property valuations.

Leading AI-Powered AVMs in Real Estate:

·         Zillow’s Zestimate: Uses deep learning to evaluate millions of listings and predict home values.

·         Redfin Estimate: Combines user-submitted data with market dynamics for refined valuations.

·         CoreLogic AVM: Trusted by financial institutions for assessing mortgage collateral risk.

By leveraging machine learning and big data, AVMs offer faster, more consistent, and scalable property evaluations, making them indispensable tools in modern real estate valuation.

 How AVMs Work

AVMs process vast volumes of data using AI-powered algorithms and statistical models such as regression analysis and machine learning. They draw insights from:

·         Comparable sales data (recent transactions of similar properties)

·         Property attributes (location, square footage, age, room count, etc.)

·         Real estate market trends and economic indicators

·         Public records, MLS data, and tax assessments

These elements combine to generate a highly accurate, data-driven property valuation.

Benefits of AI-Based AVMs in Real Estate

·         Speed: Provides instant or near-instant property value estimates

·         Cost-Effective: Significantly cheaper than manual appraisals

·         Consistency: Reduces human error and subjective bias

·         Accessibility: Easily available through online platforms

Limitations of Automated Valuation Models

·         Data Sensitivity: Inaccurate or outdated data can lead to flawed valuations

·         Lack of Contextual Insight: Cannot evaluate unique property conditions or upgrades

·         Challenges in Volatile Markets: May underperform during sudden market shifts

Common Use Cases for AVMs

·         Mortgage Lending: Lenders use AVMs for quick property valuation during loan processing

·         Real Estate Platforms: Websites like Zillow and Redfin provide AVM-based value estimates

·         Risk Management: Financial institutions leverage AVMs to monitor real estate portfolio risks

AVMs vs. Traditional Appraisals

Feature	AVM	Traditional Appraisal
Time	Instant	Several days
Cost	Low	Higher
Accuracy	Varies with data	Usually higher (human insight)
Scope	Data-driven	Data + visual inspection

 

 

The Future

With advances in artificial intelligence and access to richer datasets, AVMs are becoming more accurate and sophisticated. While they may not replace human appraisers entirely, they will continue to play a key role in property valuation processes, especially for low-risk or high-volume transactions.

 Machine Learning in Property Valuation

AI-powered machine learning models continuously improve property valuations by learning from new data inputs. They evaluate multiple parameters such as location, crime rates, transportation accessibility, and future urban development to refine property prices dynamically.

Machine learning models can identify pricing anomalies and prevent underpricing or overpricing errors. AI also considers external economic factors like inflation rates, interest rates, and employment trends to ensure accurate property valuations.

AI-Enhanced Comparative Market Analysis

Comparative Market Analysis (CMA) is a fundamental approach in real estate valuation. AI enhances CMA by analysing a broader dataset, considering multiple dimensions such as demographic shifts, zoning regulations, and emerging business hubs. AI can pinpoint undervalued properties and recommend lucrative investment opportunities based on comparative insights.

AI in Real Estate Investment Analysis

Investment analysis is crucial for investors seeking profitable real estate ventures. AI optimises investment decision-making by assessing risk factors, forecasting market trends, and personalising investment strategies.

Predictive Analytics for Investment Decisions

Predictive analytics enables investors to foresee future real estate trends and make data-driven decisions. AI-powered systems analyse historical patterns, consumer behaviour, and macroeconomic indicators to predict price fluctuations, rental yield potential, and emerging hot markets.

For instance, AI-driven heat maps visualise high-demand locations based on real-time purchasing trends, helping investors identify promising investment destinations before they peak in value.

AI-Based Risk Assessment in Investments

Investors face various risks, including price volatility, tenant defaults, and unexpected expenses. AI mitigates risks by analysing data points such as creditworthiness, local crime rates, property depreciation rates, and market saturation. AI-backed models assess financial risk and offer optimal investment strategies that align with an investor’s risk tolerance.

AI can also detect fraudulent listings or unreliable developers by cross-referencing historical disputes and inconsistencies in property records.

Portfolio Optimisation with AI

Real estate investors managing multiple properties benefit from AI-powered portfolio optimisation tools. AI evaluates asset performance, rental income stability, and market exposure to suggest strategic diversification. AI-powered platforms recommend when to buy, hold, or sell properties based on predictive analytics and economic indicators.

Advantages of AI in Property Valuation and Investment Analysis

Accuracy & Efficiency

AI reduces human errors by leveraging advanced algorithms, ensuring precise property valuations. Unlike manual assessments, AI-powered models process millions of data points instantly, expediting valuation and investment analysis.

Market Insights & Trend Forecasting

AI identifies emerging real estate trends before they become mainstream. Investors can capitalize on early-stage opportunities by analyzing AI-generated market forecasts.

Personalised Investment Strategies

AI customises investment strategies based on an investor’s financial goals, risk appetite, and property preferences. Personalised AI recommendations improve decision-making for both new and experienced investors.

Fraud Detection & Compliance

AI-driven systems detect fraudulent real estate transactions, verifying property ownership, seller credibility, and transaction history. AI ensures compliance with legal regulations, preventing risky investments.

Challenges & Ethical Considerations

Data Privacy Concerns

AI-powered valuation and investment analysis require extensive data collection, raising concerns about data security and privacy. Regulations must ensure ethical AI usage while safeguarding user information.

Bias in AI Algorithms

AI models may exhibit biases due to historical data limitations. Incorrectly trained AI algorithms can favour specific property types or neighbourhoods, potentially leading to inequitable valuations.

Dependence on AI vs. Human Expertise

While AI enhances efficiency, human expertise remains crucial in real estate decision-making. AI should be used as an assistant rather than a replacement for experienced real estate professionals.

Future of AI in Property Valuation & Investment Analysis

AI-Powered Blockchain Integration

Blockchain technology combined with AI will enhance real estate transparency and security by providing tamper-proof property records, preventing title fraud, and ensuring seamless transactions.

Smart City & Urban Planning

AI will play a major role in designing smart cities and optimising urban planning decisions, influencing property valuations through sustainable and tech-driven infrastructure development.

Advanced AI Real Estate Assistants

Future AI real estate assistants will provide end-to-end investment solutions, guiding users through the valuation, purchasing, and portfolio management process with unparalleled precision.

Conclusion

AI-powered property valuation and investment analysis are revolutionising the real estate industry by offering accurate valuations, predictive market insights, and strategic investment recommendations. Investors, real estate professionals, and homebuyers benefit from AI's ability to analyse vast data sets, mitigate risks, and optimise portfolio decisions. While AI introduces remarkable advancements, ethical considerations such as data privacy and bias mitigation must be addressed. As AI continues to evolve, its role in real estate will become even more integral, shaping market dynamics, increasing accessibility, and fostering innovative investment strategies.

What are your thoughts on AI’s impact on real estate investments? Let’s discuss!

 

What is Deep Tech?

 

What Is Deep Tech?

Deep tech, or deep technology, refers to the work of companies that develop groundbreaking innovations capable of surpassing existing technological benchmarks and pushing the boundaries of current capabilities. However, the definition of what qualifies as "deep tech" is continually evolving. As scientific and technological advancements accelerate, the criteria for what is considered cutting-edge and revolutionary also shift.

Deep tech companies are often associated with fields like artificial intelligence, biotechnology, and quantum computing, but the category extends to industries such as agriculture, aerospace, green energy, and mobility. Some deep tech companies, like Moderna, Tesla, and Impossible Foods, have become household names. Others are turning science fiction into reality—examples include SpaceX’s Starlink internet satellites and Boston Dynamics’ humanoid robots.

Regardless of the industry, deep tech companies focus on creating solutions to address some of the most urgent challenges facing humanity and the planet, such as climate change, disease, and food insecurity. Investors have taken note: according to a VC Lab report, deep tech received the highest amount of venture capital funding in 2024 and is projected to remain a top-funded sector in 2025, second only to AI and machine learning.

Deep Tech differentiators from other companies

Research and Development

R & D is the key differentiator between other technology companies and Deep tech companies. Deep tech demands substantial investments in research and development, along with a longer timeline for ROI. Additionally, some projects may turn out to be commercially or scientifically unviable. In contrast, traditional tech companies leverage established engineering methods to meet existing market demands, using ready-made software frameworks for quicker commercialization.

 Pioneering Solutions

Deep tech companies enjoy a significant competitive prowess and protection. They are well ahead of the market and face virtually no or little competition. However, these companies require a significant amount of funding and prolonged development cycles Ex: Space X and Tesla.  Because they work for innovation and breakthrough in technology.

 In contrast, the other sectors work for current market gain, and their innovation doesn’t remain unique and give rise to the new companies to follow the suite. For example, Uber the largest taxi company, has given rise to so many companies across the world.

 Creative Problem-Solving

Deep tech companies require highly skilled people with very specific areas of expertise, which is difficult to get. For example, one of the company's deep tech investors, WNT Ventures, is trying to hire electrochemists with a particular set of skills that fewer than a dozen people around the world actually possess.

Game-Changing Milestones

Conventional tech companies focus on early progress through market response and key performance indicators like customer acquisition and retention rates and revenue generation. They operate on subscription models and the user base. On the other hand, deep tech companies monitor their growth based on technological advancements and innovations they have achieved. One of the milestones for them is the number of patents they have achieved and their intellectual property. In certain sectors, achieving regulatory approvals and ensuring compliance with industry standards are also crucial markers of success.

Ultimately, the most defining metric for deep tech is its ability to propel humanity forward. The core mission of deep tech is to develop transformative innovations that unlock new possibilities and drive societal progress, reshaping the future in ways once deemed unimaginable.

Domains Deep tech companies

Deep tech companies operate in advanced technological domains that push the boundaries of innovation. These include Artificial Intelligence (AI), Machine Learning (ML), Quantum Computing, Robotics, Biotechnology, Nanotechnology, Blockchain, Aerospace, and Autonomous Systems. Industries such as healthcare, finance, defense, manufacturing, and energy benefit significantly from deep tech advancements. Here is the explanation of each of the domains.

Artificial Intelligence

 Artificial Intelligence (AI) is a field of computer science focused on creating smart systems that can execute tasks typically demanding human intelligence. These systems can mimic or even exceed specific cognitive functions of the human brain. With breakthroughs in semiconductor technology, along with advancements in machine learning and neural networks, AI is revolutionizing industries across the globe.

Machine Learning

Machine learning (ML) is a subset of artificial intelligence (AI) that enables computers to learn from data and improve performance without explicit programming. It uses algorithms and statistical models to identify patterns, make predictions, and automate decision-making. ML is widely used in healthcare, finance, e-commerce, cybersecurity, and autonomous systems. Techniques like supervised learning, unsupervised learning, and reinforcement learning power applications such as chatbots, recommendation engines, fraud detection, and self-driving cars. Advances in deep learning and neural networks have significantly enhanced ML capabilities, making it a key driver of innovation across industries and enabling smarter, data-driven solutions for complex problems.

Biotechnology

Biotechnology is the use of living organisms, such as cells and microorganisms, as well as biological processes, to develop new products. Breakthroughs in genomics and synthetic biology — a subset of biotech that involves altering the genetic material of organisms like plant and animal cells — are being used by many deep tech companies to extend the limits of what is possible in all kinds of areas.

Nanotechnology

Nanotechnology is the science of manipulating matter at the atomic and molecular scale, typically below 100 nanometers, to create innovative materials and devices. It has transformative applications in medicine, electronics, energy, environmental science, and manufacturing. In healthcare, it enables targeted drug delivery, nanosensors, and advanced diagnostics. In electronics, it drives the development of smaller, faster, and more efficient semiconductors. Nanotech also enhances renewable energy, water purification, and materials engineering. By leveraging quantum and molecular properties, nanotechnology is revolutionizing industries, leading to stronger materials, improved medical treatments, and more sustainable solutions, making it a cornerstone of future technological advancements.

Aerospace

Aerospace is a field that focuses on the design, development, and production of aircraft, spacecraft, and related systems. It encompasses both aeronautics (flight within Earth’s atmosphere) and astronautics (space exploration). Aerospace technology drives innovations in commercial aviation, defense, satellite communications, and space missions. Advancements in materials science, propulsion systems, and artificial intelligence are making air and space travel more efficient and sustainable. Companies like SpaceX, Boeing, and NASA are pushing boundaries with reusable rockets, hypersonic travel, and deep-space exploration. The industry plays a critical role in global connectivity, defense, and scientific discovery, shaping the future of transportation and space exploration

Autonomous Robots

Robotics uses a combination of science and engineering to design, construct and use mechanical robots. Using artificial intelligence, these machines can operate autonomously, and often perform tasks with greater precision, accuracy and speed than any human, making them a transformative tool across a variety of industries.

Quantum Computing

Quantum computers use quantum mechanics to solve problems that are too large or complex for traditional computers. They essentially upend the fundamental principles of computer science, relying on qubits instead of traditional bits to process information at a much faster rate. This industry is still very much in the early stages of development and utilization. But, once quantum computers reach “quantum advantage” — where they provide some time or cost advantage over classical computers — they will have a variety of commercial applications.

 Blockchain

Blockchain is a decentralized digital ledger that records transactions across a network of computers. Instead of relying on a central authority, it uses a distributed system where each node validates and updates records. Smart contracts enable automated agreement execution, while consensus protocols establish validation rules, ensuring data integrity. Once recorded, transactions become tamper-proof, fostering transparency and trust. Blockchain technology is widely used in cryptocurrency, supply chain management, finance, and secure data sharing, revolutionizing how information is stored and exchanged by enhancing security, reducing fraud, and eliminating the need for intermediaries in digital transactions.

Conclusion

Deep tech is shaping the future by pushing the boundaries of innovation and plays a vital role in society. It is also leading society to the future of technology, but it comes with unique challenges. Unlike traditional tech, which focuses on applying existing solutions to market needs, deep tech requires large investments in research and development (R&D) and a longer time horizon for a return on investment (ROI). Many projects may even prove scientifically or commercially unviable.

 The long development cycle of deep tech is evident in industries like space exploration, quantum computing, and biotech. While a mobile app can be developed and launched in months, a low-Earth-orbit satellite or an advanced AI model demands years of R&D before reaching the market. Companies investing in deep tech must navigate significant risks but also stand to redefine industries and create breakthrough innovations that shape the future.

Remote Patient Monitoring(RPM) & AI

 

With the advancement in device connectivity and high internet speed, RPM (Remote Patient Monitoring is gaining momentum and will be the way of the future. It is the service which is fast gaining acceptance and popularity in the US healthcare industry and other countries are catching up with it, the term is “Remote Patient monitoring". RPM has given a significant boost to Post-surgical care and preventive healthcare across the globe and addressed some of the pressing issues in healthcare. Providers and hospitals are using the RPM which is proving to be effective in care and cost. Remote patient monitoring (RPM) has revolutionized healthcare by enabling doctors to track patients' health from a distance. Remote patient monitoring(RPM) collects, analyzes and stores health information through live monitoring via devices such as (sensors, Wearable devices, and Blood pressure monitoring machines) or Mobile applications that transmit information from the home or care facility to a provider that either stored in a local facility or cloud. Further, with the integration of Artificial Intelligence (AI), RPM is becoming more efficient, accurate, and personalized. AI-powered RPM is changing the way healthcare professionals diagnose, treat, and manage chronic conditions, ultimately leading to better patient outcomes and reduced hospital visits.

The data collected by devices is later used by providers to monitor the patient's health condition and track improvement. The data collected can also provide alerts when patient health is not improving and go to Doctors, hospitals, and clinics. RPM was already gaining momentum, and post-pandemic, it's getting attention in the healthcare world. RPM has gradually become a very lucrative option for hospitals and clinics.

Remote patient monitoring is primarily used in critical care such as Patients suffering from Diabetes with critical levels, High/low blood pressure, and post-surgery monitoring (patients underwent major surgeries and required monitoring for some time. Doctors, with the help of devices, collected the patient data at regular intervals and analysed and provided treatments according to the data results.

Patient data received through various devices can also be stored in the cloud and later shared with doctors for early diagnosis. Based on the health data shared by the patient's devices, a health coach or caregiver is assigned to the patient, who guides the patient in medication, exercise, and wellness.

Example: In cardiac care Patients with advanced, AI-enabled pacemakers can share their measuring parameters with their Drs/Nurses without going to the clinic. The continuous flow of the data has improved the patient monitoring and helping Drs. To take the right decisions at patient crucial stages such as heart attacks, and hypertension. Continuous monitoring of patient data also allows Dr. to predict the patient's health and the precautions he/she needs to take.

Market Size

Providers, clinics, and hospitals have significantly increased their implementation of RPM in the US and globally. According to GVR(Grand View Research), the global remote patient monitoring market was estimated at USD 5.2 billion in 2023 and is expected to register a compound annual growth rate (CAGR) of 18.6% from 2024 to 2030. 

Image 1: US (RPM) remote patient monitoring systems market

However, remote patient monitoring with the use of Artificial intelligence (AI) research is happening to make the tools better and to get better insights and reports also with the AI and predictive analysis helping Drs. To predict the health of the patient after getting treatment. Research is happening on devices such as (Sensors, and other electronic devices) to get the precise vital readings of the patient and accurate to increase the effectiveness of the devices and also improve their connectivity by making the RPM more effective and efficient.

North America is a leading player in the remote patient monitoring system industry in 2023, accounting for more than 41.37% of the total market share. The rise of chronic diseases has pushed the demand for wireless and portable systems along with the presence of reimbursement structures aimed at cutting expenditure are the major factors attributed to growth.

Advantages of RPM

Real-time Data Analysis and Predictive Insights

Traditional RPM devices collect vast amounts of patient data, but AI takes it a step further by analyzing this data in real-time. Machine learning algorithms can detect abnormal patterns in vital signs, such as heart rate, blood pressure, or glucose levels, and alert healthcare providers before a condition worsens. Predictive analytics can also forecast potential health risks, allowing doctors to take proactive measures.

Enhanced Chronic Disease Management

AI-driven RPM is particularly beneficial for patients with chronic diseases like diabetes, hypertension, and heart disease. Smart wearables and connected devices continuously monitor patient vitals and use AI to offer personalized recommendations. For instance, AI-powered insulin pumps adjust insulin doses automatically based on real-time glucose readings, reducing the risk of complications.

Improved Patient Engagement and Adherence

AI-enabled RPM systems encourage better patient engagement by offering real-time feedback, reminders, and coaching. Chatbots and virtual health assistants powered by AI help patients stay on track with medication schedules, lifestyle changes, and follow-up appointments. By providing educational insights and motivation, these tools enhance adherence to treatment plans.

Remote Diagnostics and Virtual Consultations

Telemedicine combined with AI-powered RPM allows doctors to diagnose and treat patients remotely. AI algorithms analyze patient symptoms, medical history, and current health data to provide diagnostic suggestions. This reduces the need for frequent hospital visits, making healthcare more accessible, especially for patients in rural or remote areas.

Early Detection of Health Issues

AI’s ability to detect early signs of diseases is transforming preventive healthcare. By continuously analyzing physiological data, AI can identify deviations that may indicate the onset of conditions such as atrial fibrillation, sepsis, or respiratory distress. Early detection enables timely interventions, preventing complications and reducing healthcare costs.

Integration with Smart Wearable and IoT

The rise of smart wearable’s and Internet of Things (IoT) devices has enhanced the capabilities of AI-driven RPM. Devices such as smartwatches, biosensors, and connected ECG monitors continuously collect and transmit health data to AI systems. These AI models process the data to provide real-time insights and recommendations for both patients and healthcare providers.

Enhanced Security and Data Privacy

AI also plays a crucial role in securing patient data. With advanced encryption and anomaly detection, AI enhances cyber security in RPM systems, preventing data breaches and ensuring compliance with regulations such as HIPAA and GDPR. AI-driven authentication methods, such as biometric verification, further strengthen patient data protection. The importance of remote patient monitoring (RPM) has been observed during the COVID-19 pandemic. It has helped doctors to monitor their patients without physical contact and this also has minimized the risks of spreading the virus. Remote patient monitoring has changed the healthcare monitoring device industry dynamics the healthcare devices market is continuously growing with a CAGR of 18% by 2030.

Image 2: Healthcare device market size

Healthcare technology is evolving and new research is happening every day and making RPM more effective for patients. As a result of that now sensors are being used with devices and smartphones along with AI to gather the patient's vital data. Sensors have given the freedom to patients from wearing the devices and track their vitals, such as ECG, vitals of pre-mature born babies, heart rate, etc. These sensors also have a range to capture the patient's data so, patients have to be within the range for data to be captured. Apart from sensors, other technologies are also used in combination with sensors are cameras and Smartphones to capture the patient's vitals.

Smartphones

Apart from RPM devices, Smartphone technology has improved significantly Smartphones are now equipped with AI and the sensors in mobile phones have significantly improved, and that has led to rapid growth in the field of telemedicine and patient monitoring applications. Telemedicine applications have emerged to complete telehealth solutions.

Smartphones are equipped with AI tools and can be easily integrated with the SDKs of different devices for smooth data transfer to the doctor. The Patient data can be shared with the Drs. on a real-time basis. Smartphone application also helps in maintaining patient’s vital data both on the device and on the cloud. Smartphones also provide two-way communication that helps Dr. to reach out to the patients; apart from this Smartphones are easy to use by patients and help patients in self-care.

With the increasing use of RPM, there is a rise in Home patient monitoring, which allows healthcare providers to monitor patient's health conditions in the home. With the invention of Sensors, wearable devices, and Smartphones it is easy to track patient vitals without him visiting hospitals/clinics. In this pandemic time, Home patient monitoring keeps a lot of critical and elderly patients safe at home. This also allows a lot of communication between doctors and patients and patients are also becoming an active contributor to their treatments.

Conclusion

Remote patient monitoring is going to have a significant impact and pave the way to new kinds of patient care. The RPM in due course of time is becoming more advanced and will be gaining more acceptance as an integral part of patient treatment and care. The future of remote patient monitoring will be the way of the future.