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Banking has seen rapid experimentation with generative AI, especially in automating customer interactions, streamlining lending, and assisting employees with data and documentation. Community and regional banks in the U.S. have leveraged external AI platforms to gain efficiency advantages, while larger institutions worldwide are rolling out custom GPT-powered copilots at scale. Small and Mid-Sized Banks: Efficiency and Customer Engagement Bankwell Bank (Connecticut, USA) – Small Business Lending Copilot:This $2 billion community bank piloted a generative AI virtual lending assistant to guide small-business owners through the loan application process. Developed with startup Cascading AI, the assistant “Sarah” converses with applicants, collects necessary data (e.g., years in business, loan purpose), and performs initial qualification tasks off-hours. It can follow up with applicants who drop off mid-application – for example, contacting a borrower on a Friday night to clarify a missing EIN (tax ID) and help them continue the form. Early results are promising: about 90% of the loan application process is now automated by the AI (from data gathering to pre-approval), with human loan officers only stepping in to review and finalize decisions. This has significantly accelerated lending workflows – what used to take weeks of back-and-forth now happens in real time, even after business hours. By keeping a human-in-the-loop for oversight, Bankwell mitigates risks of AI error while still reaping major efficiency gains. SouthState Bank (Florida, USA) – Internal Knowledge Chatbot:Regional lender SouthState (approx. $40B in assets) was one of the first banks to deploy a GPT-based internal assistant, nicknamed “Tate,” to augment employee productivity. Built on Microsoft Azure OpenAI (ChatGPT) and Azure Cognitive Search, Tate was launched in early 2024 as an internal Q&A chatbot ingesting the bank’s policies, product info, and procedures. In just the first weeks, SouthState observed measurable ROI: at a development cost of around $25K and running cost around $50/day per 100 users, Tate could save about 5,200 staff hours per year (for 100 employees asking 2 questions/day) – equivalent to roughly $442,000 in productivity savings. This is an 11.6× return in the first year, with a payback period of just one month. Frontline staff use the chatbot as an always-available “team of researchers” for instant answers, reducing time spent searching manuals or waiting on email responses. The result is faster customer service and more consistent, accurate information provided to clients. Such internal copilots also shorten training time for new hires by surfacing needed knowledge on demand. SouthState’s success demonstrates that even mid-sized banks can affordably deploy enterprise-grade LLM solutions via cloud providers, yielding outsized efficiency gains. ORNL Federal Credit Union (Tennessee, USA) – Fair Lending Analyst:This mid-sized credit union (with $3.7B in assets) is piloting “LuLu,” a generative AI assistant focused on fair lending compliance and analytics. Partnering with fintech Zest AI, ORNL FCU is using LuLu to analyze lending data and answer complex questions about loan portfolios, with the goal of identifying any unintended bias or opportunities to expand credit access. Launched in Q1 2024, LuLu was first trained on 15 years’ worth of anonymized lending queries and industry data, and then tailored to ORNL’s own loan portfolios and internal reports. Business Impact: Executives can ask the AI things like “How do our auto loan approval rates for minority borrowers compare to peers?” and get nuanced, data-backed answers in seconds. ORNL’s CEO sees this as a way to “serve the underserved” by continually testing what-if scenarios – for example, “What if we adjusted policy X – would more qualified borrowers from protected classes be approved, without increasing risk?” By rapidly crunching historical data and outcomes, the AI helps ORNL proactively refine underwriting rules to increase inclusivity while controlling risk. This pilot underscores a novel use of generative AI in banking: not just boosting efficiency, but also furthering compliance and ethical lending goals through better insight. Large Banks and Global Leaders: Scaled Co-Pilots and Customer Experience Morgan Stanley Wealth Management (USA) – Advisor Assistant & Note-Taker:Morgan Stanley, a global leader in wealth management, has been at the forefront of applying OpenAI’s GPT-4 to its business. In mid-2024, it rolled out AI @ Morgan Stanley “Debrief,” an OpenAI-powered copilot for its 16,000 financial advisors. Debrief can join client meetings (with consent) via videoconference, automatically transcribe the conversation, highlight key points, and draft follow-up emails and action item summaries. It even logs meeting notes into the Salesforce CRM, saving advisors from manual data entry. This tool is already hugely popular – over 98% of advisory teams adopted the AI within weeks of launch. On average, it saves each advisor about 30 minutes per client meeting on note-taking and documentation. For an advisor handling 4–5 meetings a day, that translates to 2+ hours freed daily, allowing more time for client engagement and planning. Morgan Stanley’s embrace of GPT-4 (delivered via Azure OpenAI) is not limited to Wealth Management; in late 2024 the firm expanded these generative AI tools to its investment banking and trading divisions as well, reflecting a broad bet on AI to drive productivity across research, sales, and trading. Jeff McMillan, the firm’s Head of AI, envisions such copilots as an “efficient interaction layer” between employees and information systems, streamlining workflows that previously ate up hours. Barclays (UK) – Frontline Customer Service AI & Cloud Platform:Major banks in Europe are likewise investing in generative AI capabilities. Barclays’ Group CIO Craig Bright noted in January 2024 that the bank is using AWS’s Bedrock generative AI service to enhance frontline customer engagement. For example, Barclays is developing AI assistants to help call center staff quickly retrieve answers for customers by querying internal knowledge bases – similar to an internal ChatGPT trained on Barclays’ products and policies. The bank is also rolling out an employee-facing chatbot called DBS-GPT (separate from DBS Bank) to help its 30,000 technologists with coding and content generation tasks, improving developer productivity. These efforts are part of Barclays’ strategy to “fuel AI through data”; with 48 million customers, Barclays is leveraging its huge data stores to give AI rich context, enabling highly personalized and current insights for both employees and clients. While specific metrics are not yet public, Barclays expects generative AI to significantly improve customer support resolution times and consistency, and internally they anticipate notable efficiency gains for their IT and operations teams. The bank is also mindful of risks – for example, using AI to augment (not replace) human judgment in fraud detection and credit decisions – aligning with Bright’s view that AI’s role is to “bring a higher standard to the work [people] do” rather than operate unchecked. Asia-Pacific Leaders (Singapore) – Enterprise AI Integration In Singapore, which has become an AI innovation hub, banks like DBS are “industrializing” AI across the enterprise. DBS Bank announced it will equip its 500 customer service officers with a GenAI virtual assistant by the end of 2024. The assistant can live-transcribe customer calls and auto-suggest solutions by searching the bank’s knowledge base in real time, helping service staff resolve inquiries faster. DBS also built an internal ChatGPT-based tool (“DBS-GPT”) for employees to generate reports and first drafts of emails securely. These initiatives aim to improve response times and reduce manual workload – for example, routine customer queries can be answered 50% faster when agents use the AI suggestions. Similarly, OCBC Bank and others in APAC are piloting GPT-powered bots to assist with compliance checks and generate internal research summaries. Singapore’s regulators have encouraged such trials under sandbox conditions, as banks here see generative AI as a “game changer” for scaling personalized services while maintaining strict risk controls. Table 1: Selected Case Studies – Generative AI in Banking & Finance (2024–25) Organization (Sector)Generative AI ApplicationTechnology ProviderOutcome / ImpactBankwell Bank (US community bank)Virtual loan officer assistant for SMB lendingCascading AI (startup LLM)Automates ~90% of loan process, cutting weeks of back-and-forthSouthState Bank (US regional bank)“Tate” internal Q&A chatbot for employeesMicrosoft Azure OpenAI (GPT-4)~$442K annual productivity savings per 100 users (5,200 hours)Morgan Stanley Wealth (Global WM)“AI @ MS Debrief” – meeting notes & email draftsOpenAI GPT-4 via AzureSaves ~30 min of admin work per client meetingAIG (Global insurer)Underwriting document processing & analysisProprietary GenAI ecosystemData intake and accuracy in underwriting improved from ~75% to 90%.Sedgwick (Global claims admin)“Sidekick” claims doc summarization (30+ pages)OpenAI GPT-498%+ summarization accuracy on ~50K claim documents. Reports generated in minutes instead of hours, reducing claims resolution time and manual review burden.ERGO Insurance (EU insurer)Customer service virtual agent (24/7 support)Azure OpenAI (via EBO bot)Deployed in just 4 months, handling thousands of customer queries in natural language. Improved response times and 24×7 availability. Table 1: Real-world examples of generative AI implementations in banking, insurance, and financial services, with technologies and outcomes.

AI in diagnostics is taking center stage, reaching far beyond traditional imaging into pathology, lab testing, genomics, and specialized disease screening. By harnessing machine learning to interpret a wide spectrum of clinical data—from medical imagery and genetic sequences to lab results and patient-reported symptoms—these solutions enable faster, more accurate disease detection. Powerful algorithms now pinpoint cancer cells in pathology slides, while predictive models flag subtle indicators of chronic illness through routine test patterns. In this article, we’ll explore global innovations shaping diagnostic AI, the underlying technologies driving them, and the increasing relevance of these advances for healthcare leaders in India. Global Examples of AI-Powered Diagnostics Digital Pathology & Cancer Detection: Advanced AI algorithms in pathology help pathologists analyze biopsy slides for cancers and other diseases. For instance, Aiforia (Finland) offers CE-marked AI models for pathology; one model helps detect and quantify PD-L1 biomarkers in lung cancer tissue, assisting in identifying patients eligible for immunotherapy. In the UK, the NHS has begun adopting such tools via the PathLAKE consortium, indicating trust in AI for clinical diagnostics. Similarly, PathAI (USA) and Paige AI (USA) have developed deep learning models that can scan whole-slide images to find microscopic tumor foci or grade cancers, improving accuracy and reducing workload for pathologists. Early studies show these tools can shorten analysis time and reduce missed lesions when used as decision support. Genomic Diagnostics: AI is also accelerating genomic analysis. Companies like Haystack Analytics (India) leverage AI to interpret complex genetic data for personalized medicine. By sifting through thousands of genetic variants, AI can help pinpoint mutations responsible for hereditary diseases or cancer subtypes much faster than manual methods. This is critical as genomics grows in importance for diagnosing rare diseases and tailoring treatments. In Japan, researchers are using AI on genomic and clinical data to predict disease risk likelihood of developing conditions like diabetes or certain cancers by analyzing genetic markers combined with routine health data. These AI models often employ advanced pattern recognition and even federated learning to aggregate insights from multiple databases without compromising privacy (more on federated learning later). AI for Routine Lab Tests: Beyond complex genomics, AI is improving everyday lab diagnostics. A notable example is Piiko.io (India), which built a compact, AI-enabled machine for routine blood tests. Piiko’s device automates sample processing and uses AI-driven analytics to perform common tests (like CBC and biochemistry) at low cost. By optimizing reagent use and interpreting results with machine learning, it makes quality diagnostics accessible to smaller labs that cannot afford large analyzers. This kind of innovation illustrates how AI can democratize diagnostics by reducing cost and expertise barriers. Oncology Diagnostics (Beyond Radiology): Several AI tools help detect cancers via endoscopy and other non-imaging diagnostics. In Japan, startup AI Medical Service (AIM) developed an AI system for endoscopy that analyzes live video from gastroscopy/colonoscopy to highlight suspicious lesions. Trained on over 200,000 endoscopic videos, the AI can flag potential early-stage cancers in real-time, helping endoscopists not to overlook subtle lesions. This is a boon in Japan, which has one of the world’s highest endoscopy rates for gastric cancer screening. The AI operates at high speed (analyzing an image in 0.02 seconds) and with ~94% accuracy in detecting cancer presence, showcasing how computer vision can assist even in optical diagnostics. Notably, the final diagnosis remains with the physician, and such tools are viewed as “second readers” to improve accuracy. Similarly, Iris Healthcare (Japan) created an AI device called “nodoca” to diagnose influenza by analyzing throat images and symptoms. Nodoca uses deep learning on 500,000 throat images and patient data to identify signs of flu within seconds, offering a pain-free alternative to swab tests. It exemplifies AI combining visual analysis with patient-reported data for rapid point-of-care diagnostics. Specialty Screening (Ophthalmology & Cardiology): AI is excelling in screening for diseases in specialties like eye care and cardiology. In ophthalmology, automated retina scanners with AI can detect diabetic retinopathy or glaucoma early. Singapore’s national diabetic eye screening program uses an AI called SELENA+ (Singapore Eye Lesion Analyzer) to evaluate retinal photographs for disease; this system is deployed in a semi-automated mode to balance cost-effectiveness with accuracy. The AI flags cases that likely have disease for human specialists to review, greatly increasing productivity in screening large populations. In India, startups Artelus and Remidio have similar AI tools: Artelus provides deep learning models for diabetic retinopathy screening, and Remidio’s fundus-on-phone device can capture retinal images and automatically generate diagnostic reports on the smartphone. These innovations allow screening in remote clinics by non-specialists – a critical need in countries with uneven specialist distribution. In cardiology, AI-driven devices like the Japanese “super stethoscope” by AMI are emerging; this device records heart sounds and ECG in 10 seconds and uses AI to detect cardiac abnormalities, enabling remote diagnosis of heart disease. India’s Tricog Health has taken a related approach by using AI to interpret ECGs – Tricog’s platform analyzes ECG and echocardiogram data to identify heart attacks or arrhythmias and has touched over 10 million patients globally, illustrating how AI can scale expert cardiology interpretation to primary care settings. Technologies Powering Diagnostic AI Modern diagnostic AI solutions are driven by a robust set of technologies tailored to specific data types and use cases. Deep Learning, particularly convolutional neural networks (CNNs), is the backbone of image-based diagnostics. These models excel at analyzing pathology slides, retina scans, and endoscopic videos by identifying complex visual patterns that may elude human eyes.Natural Language Processing (NLP) plays a crucial role in parsing patient symptoms, clinical notes, and correlating textual data with imaging—helping systems derive deeper clinical insights.Predictive Analytics and Classical Machine Learning are widely used to assess structured data, such as lab test results and vital signs, enabling early warning systems for cardiac events and chronic illnesses.A notable innovation is Federated Learning, which enables AI models to be trained across decentralized hospital systems without sharing raw patient data. This privacy-preserving method allows for broader and more diverse datasets, improving model performance while safeguarding patient confidentiality. Projects like Eye2Gene have shown promising results using this approach to predict genetic mutations with high accuracy. India’s Growing AI Momentum in Diagnostics India’s diverse healthcare landscape presents both opportunities and challenges—and diagnostic AI is rising to meet them. Niramai Health Analytix (Bangalore) is pioneering AI-based thermal imaging for early breast cancer detection. Their solution, Thermalytix, is portable, radiation-free, and operable by non-doctors, making it ideal for remote screening. It’s a prime example of frugal innovation—reducing costs while increasing access and impact, with the potential to improve survival rates by up to 4x through early detection.Onward Assist (Hyderabad) offers an oncology decision-support platform that leverages AI to analyze histopathology images and reports. By prioritizing high-risk cases and quantifying tumor features, Onward has helped pathologists reduce reporting time by 30–40%—a game-changer for overburdened public hospitals.The Tamil Nadu e-Governance Agency has developed ePaarwai, an AI-based mobile app for large-scale cataract screening. Using just a smartphone camera, it enables frontline health workers to detect cataracts in remote areas and refer only the confirmed cases for surgical consultation—optimizing limited specialist resources.National initiatives like the Genome India Project and IndiGen are building indigenous genomic databases. These will serve as foundational data sources for AI models customized to India’s population, enabling precision diagnostics in hereditary and rare diseases.The Indian Council of Medical Research (ICMR) has laid the groundwork for ethical AI development in healthcare by releasing comprehensive guidelines in 2023, signaling regulatory readiness for AI integration at scale. Tailored, Localized AI in Action Indian startups are designing AI solutions that reflect the country’s unique healthcare needs: Artelus and Remidio have developed AI-based diabetic retinopathy screening tools suitable for rural deployment. Remidio’s fundus-on-phone system enables retinal imaging and diagnosis via smartphone, empowering non-specialist health workers in resource-limited settings.DeepTek (Pune) provides “Augmento,” an AI platform that supports radiologists in interpreting X-rays and CT scans. With deployment in over 200 centers, it’s helping scale radiology services where expertise is scarce.Tricog has revolutionized cardiology diagnostics by using AI to interpret ECGs and echocardiograms across thousands of clinics. Their platform has already touched over 10 million patients, enabling faster, more accurate heart attack detection—especially in primary care settings. Global Benchmarks: How Leading Nations Are Shaping Diagnostic AI Learning from countries with advanced healthcare systems offers a strategic lens for integrating AI into diagnostics. Each has adopted AI thoughtfully, aligning technology with infrastructure, clinician workflows, and patient needs. 🇬🇧 United Kingdom The UK’s National Health Service (NHS) has been a global front-runner in adopting diagnostic AI. Initiatives like NHSX AI Lab and the PathLAKE consortium have funded large-scale trials across diagnostic workflows. A notable example is the MASAI trial, one of the world’s first real-world validations of AI in breast cancer screening. Conducted in Sweden in collaboration with the UK, the study found that AI could safely replace one of two radiologists in mammogram double-reading—detecting more cancers while cutting workload by nearly 50%. In pathology, NHS trusts are adopting tools like Aiforia’s PD-L1 analysis, reinforcing institutional trust in AI as a clinical decision support tool. The UK’s approach emphasizes rigorous validation and real-world evidence before widespread deployment—an approach India is beginning to mirror through pilot programs and data banks like the Tata Medical Center’s cancer image repository. 🇯🇵 Japan Japan is witnessing a wave of clinician-led AI innovation in diagnostics. Startups like AI Medical Service and Iris Healthcare have built AI tools for endoscopy-based cancer detection and non-invasive flu diagnosis, respectively. Culturally, Japan has positioned AI as a clinical assistant, not a replacement. This framing has led to broader physician acceptance. On the regulatory side, Japan has taken a proactive approach by building a national roadmap for AI in medical devices—something India has begun to emulate through ICMR’s ethical guidelines for healthcare AI. 🇸🇬 Singapore Singapore demonstrates the power of integrating AI into national health programs. In its diabetic retinopathy screening initiative, SELENA+ AI is embedded into public clinics to scan retinal images. The model flags high-risk cases for human review—allowing clinics to screen more patients while maintaining accuracy. Singapore’s strategy balances automation with cost-effectiveness through semi-automated workflows, which could serve as a blueprint for India’s public healthcare programs. The city-state also uses predictive AI to identify inpatients at risk of readmission, enabling preemptive interventions. 🇸🇪 Scandinavia Nordic countries—Sweden, Denmark, and Finland—are leaders in digital pathology and system-level AI deployment. Sweden has launched several regional AI programs for chest X-ray analysis and mammography screening. Denmark’s largest healthcare region recently moved to a cloud-based imaging infrastructure, enabling centralized deployment of AI tools across hospitals. This reflects a platform approach, consolidating data to maximize AI’s diagnostic utility. While India’s fragmented hospital IT landscape is a challenge, initiatives like the ABDM’s National Health Stack aim to lay a similar foundation for nationwide interoperability. Key Takeaways for India These global examples underscore four critical success factors for diagnostic AI adoption: Robust Digital Infrastructure – From imaging systems to data integrationRegulatory Clarity – Clear pathways for AI validation and certificationClinician Buy-In – AI as a support tool, not a replacementWorkflow Alignment – Seamless integration into day-to-day operations For India, a strong public-private partnership model will be vital to replicate these successes. Encouragingly, momentum is building. Challenges in Scaling Diagnostic AI in India Despite growing adoption, India faces unique hurdles in bringing diagnostic AI to scale: 1. Data and Infrastructure Gaps Many Indian labs still operate with analog systems—paper records, non-digital microscopes, or standalone machines. Without digital infrastructure, AI cannot be deployed. Public investment is needed in digital pathology scanners, cloud or edge computing capabilities, and integrated lab systems. 2. Regulatory Uncertainty The 2023 ICMR guidelines are a starting point, but more clarity is needed on how AI tools are certified and monitored in clinical settings. Hospitals remain cautious due to liability concerns. There’s also a need for localized validation—AI models trained on Western datasets may not perform optimally on Indian populations. 3. Economic and ROI Pressures High upfront costs for advanced AI solutions remain a barrier, especially for budget-conscious healthcare providers. For AI adoption to grow, vendors must offer flexible pricing models—such as pay-per-use, cloud-based deployments, or bundled solutions. ROI must be proven through metrics like time saved, diagnostic accuracy, or reduced unnecessary tests. 4. Cultural Acceptance and Training Doctors and patients alike may view AI with skepticism. The key is building trust—through explainable AI (e.g., visual overlays showing what the AI “saw”) and hands-on training for clinicians and technicians. Positioning AI as an assistant—not a threat—will accelerate acceptance. 5. Privacy and Ethical Safeguards AI in diagnostics handles highly sensitive data like biopsy images or genomic profiles. With India’s data protection laws still evolving, strong safeguards are essential. Federated learning and on-premise deployments can help mitigate risks. Adhering to ethical principles around fairness, accountability, and transparency is also crucial. The Road Ahead: Building an AI-Enabled Diagnostics Ecosystem Despite these challenges, India is heading in the right direction. Government support for “Make AI in India, Make AI work for India” reflects strong national intent. With the rollout of digital health IDs under ABDM and increasing collaboration between tech firms, hospitals, and regulators, the foundation is being laid for a robust, AI-powered diagnostic ecosystem. By embracing innovation while addressing ground realities, India has the opportunity not just to catch up—but to lead—in scalable, equitable diagnostic care.

Beyond direct clinical diagnosis, AI is increasingly used to streamline hospital operations and workflows – the myriad processes that keep a hospital running, from appointment scheduling and patient triage to supply chain and record-keeping. In many healthcare systems, especially in large public hospitals, operational inefficiencies lead to long patient wait times, clinician burnout, and suboptimal care coordination. AI-driven workflow automation aims to alleviate these by using data-driven algorithms to predict needs, automate routine tasks, and optimize resource allocation. This domain encompasses predictive analytics for hospital management, intelligent scheduling systems, AI-assisted documentation, and even the use of robotics for logistical tasks. Below we examine global real-world applications and how similar approaches could benefit Indian hospitals. Global Examples of AI in Hospital Operations Intelligent Appointment Scheduling & No-Show Reduction: Missed appointments and long waitlists are major issues in hospitals worldwide. The NHS in the United Kingdom recently piloted an AI system to tackle this by predicting no-shows. Developed by a UK startup (Deep Medical) in partnership with clinicians, the software analyzes various factors – appointment history, demographics, even weather and traffic – to predict which patients are likely to miss appointments. It then proactively offers those patients alternative slots (like evenings/weekends) and fills their original slot with back-up bookings. In a six-month pilot at an Essex NHS Trust, this AI-driven approach cut “Did Not Attend” rates by almost 30%, enabling an extra 1,900+ patients to be seen and projecting annual savings of ~£27.5 million for that trust. Following this success, the NHS is scaling it to more hospitals. This example highlights how predictive analytics and simple automation (rebooking appointments) can dramatically improve workflow and efficiency. In the US, similarly, hospitals like UT Southwestern have used AI to optimize operating room schedules, but focusing on UK/others: some NHS hospitals also use AI to prioritize elective surgery waitlists by clinical urgency and likely cancellation risk, ensuring operating rooms are utilized optimally. Patient Flow and Bed Management: Managing patient flow – admissions, discharges, and transfers – is a complex juggling act. AI tools are helping hospitals forecast patient volumes and allocate beds/staff accordingly. For example, a hospital in Canada used an AI-based system to predict emergency department arrivals and inpatient bed demand, allowing them to adjust staffing in advance. This led to a reported 50% reduction in ER wait times by better matching capacity to demand. In Singapore, the public health system’s analytics unit (IHiS) deployed a machine learning model that, upon a patient’s hospital admission, predicts their risk of needing re-admission within 30 days after discharge. This model helps care teams target extra attention (like discharge planning and follow-up calls) to high-risk patients (particularly complex elderly cases), thus smoothing the workflow by potentially preventing avoidable re-admissions. Interestingly, in designing this AI, Singapore’s team chose to sacrifice a bit of predictive accuracy to ensure the system was fair and usable – they adjusted the algorithm to prioritize including patients in interventions (sensitivity) even if it meant a few more false alarms, as this aligned with their care inclusion goals. This shows that in workflow AI, the best model isn’t just about accuracy, but about aligning with health system priorities (in this case, inclusivity and patient engagement). Surgical Scheduling Optimization: Operating theatres are one of the most expensive hospital resources. In Europe and Asia, hospitals are piloting AI to optimize surgical schedules. For instance, in London, an NHS hospital worked with an AI firm to analyze years of surgical data (case length, surgeon availability, etc.) and build a predictive scheduling system. The AI could dynamically adjust schedules in real-time if a surgery was running long or an emergency case arose, automatically suggesting reallocation of cases to different theatres or times. A hospital in Manchester reported that after implementing a similar AI scheduler, they saw fewer last-minute cancellations and better utilization of operating rooms (less downtime). These systems often use reinforcement learning or advanced optimization algorithms to constantly improve scheduling efficiency. Over time, the AI learns patterns (for example, certain surgeons consistently finish faster than scheduled, or late afternoon cases have higher cancellation rates) and uses that to make smarter booking templates. The result is not only cost savings but also reduced stress on staff and patients (through shorter wait times for surgery). Administrative Automation (NLP for Documentation): A significant portion of clinicians’ time is spent on documentation – writing case notes, discharge summaries, filling forms. AI-powered natural language processing is now being deployed to automate some of these tasks. In many advanced hospitals, voice recognition (like Nuance’s systems, now part of Microsoft) transcribes doctors’ dictated notes in real-time. The next frontier is Generative AI that can summarize consultations or create first drafts of discharge instructions. For example, in Denmark, some hospitals are experimenting with AI assistants that listen in during patient visits (with consent) and automatically generate the clinical note and even draft prescription orders for the doctor to review. While specific country examples outside the US are limited (the US has multiple pilots like this), countries like the UK are actively exploring such tech – NHS England’s AI Lab has funded projects to use large language models for collating data from various documents to ease administrative burden. This kind of workflow automation doesn’t directly involve patients but can significantly free up doctors’ and nurses’ time to focus on care. Early reports suggest that these AI scribes can save doctors several hours a week in paperwork. Hospital Logistics and Robotics: In high-income places like Singapore and Japan, hospitals are even employing autonomous robots to handle logistical tasks, reducing the burden on staff. A prime example is Changi General Hospital in Singapore, which deployed Panasonic’s HOSPI robots as early as 2015 – these are autonomous mobile robots that ferry medications, laboratory specimens, and documents around the hospital 24/7. The robots navigate hospital corridors and even use elevators on their own, carrying items securely (accessible only via ID cards). This addresses manpower constraints, especially as Singapore faces an aging healthcare workforce. In Japan, which also grapples with staff shortages, robotics is used in both logistics and patient-facing roles. One notable collaboration involved a robotic telesurgery trial between clinicians in Singapore and Japan – while that’s more clinical, in hospital ops we see Japanese hospitals using robots for cleaning and patient guidance. For instance, some Japanese facilities use concierge robots in lobbies to direct patients (multilingual AI chat interfaces to answer common queries), and delivery robots similar to HOSPI for nightly medication distribution from pharmacy to wards. These robots incorporate AI for navigation (computer vision to avoid obstacles, mapping algorithms) and sometimes voice recognition to interact. While not every hospital needs physical robots, they represent how far automation can go in reducing routine human tasks. Technologies Used: The backbone of hospital workflow AI is often predictive analytics – using classical machine learning (regression, time-series forecasting) or modern deep learning on hospital data (like appointment logs, EHR data, staffing records) to predict future operational needs. For scheduling and resource optimization, operations research algorithms combined with machine learning (for prediction) are common. Natural language processing (especially large language models in recent times) is crucial for automating text-heavy tasks like documentation or responding to patient inquiries (chatbots for hospital helplines). Computer vision and robotics come into play for physical automation (e.g., a robot “sees” its environment, or an AI analyses CCTV to detect if a bed is free). A noteworthy approach for multi-hospital systems is the use of cloud platforms that integrate AI: e.g., as mentioned, Denmark’s Capital Region moved to a unified cloud system that can host AI modules for all hospitals, making scaling easier. We also see federated analytics in some contexts – for example, multiple hospitals might share insights on patient flow without sharing patient data, to improve overall algorithms (less common so far in ops, more in clinical data sharing). Relevance to Indian Hospitals Indian hospitals, particularly large public hospitals and busy private hospitals, face chronic workflow challenges – overcrowded outpatient clinics, long wait times for surgeries, high rates of missed follow-up, and overworked staff. AI-based workflow automation could offer much-needed relief by optimizing processes and better utilizing limited resources: Reducing Wait Times and Crowding: AI can help manage India’s notorious outpatient department (OPD) rush. Consider a large government hospital in a metro city where hundreds line up at dawn for token numbers. An AI scheduling system (like the NHS no-show predictor) could be adapted: using historical attendance data to overbook slightly where many no-shows occur, or to schedule patients in tighter windows if it predicts low turnout on a rainy day, for example. Even simple triage kiosks with AI chatbots (in local languages) at hospital entry could categorize patients by urgency before they see a doctor. This is analogous to how some emergency departments worldwide use AI to predict which arriving patients are high-risk. In India, a triage bot could ask basic questions and flag, say, possible heart attack or stroke symptoms to prioritize those patients in the queue. Some Indian startups are developing such triage assistants using NLP and symptom databases. Implementing these could prevent critical cases from waiting unattended in general lines.Appointment and Follow-up Management: In India’s context, missed appointments often translate to patients simply not getting care (since there isn’t always a formal rescheduling system). Private hospitals with appointment systems could deploy AI similar to the NHS pilot – predicting no-shows and sending reminders or auto-rescheduling. Additionally, AI could be used to identify patients who are likely to drop out of treatment (common in chronic disease clinics). For example, an AI might analyze socio-economic data and past behavior to predict which TB patients won’t follow through the 6-month treatment; health workers can then target those patients for extra counseling or follow-up calls. This crosses into public health, but it’s a workflow improvement: focusing finite outreach resources where they are most needed. Indian healthcare providers, including government programs, could greatly benefit from such preventive operations management.Staff and Bed Optimization: Many Indian public hospitals face erratic patient loads and limited ICU or ward beds. AI forecasting models, trained on seasonal illness trends and real-time syndromic surveillance, could help predict surges (e.g., a dengue outbreak or seasonal flu spike) and trigger proactive resource allocation – setting up extra fever clinics, stocking supplies, arranging temporary beds. A form of this was seen during COVID-19 waves, where some hospitals used rudimentary models to predict caseloads. Institutionalizing this with AI can make surge management more routine. Some forward-looking private hospital chains in India are exploring “command centers” where AI dashboards display live predictions of bed occupancy, emergency cases expected, etc., to guide administrative decisions. This is inspired by models in places like Johns Hopkins (USA) or NHS’s Control Centers (like in Liverpool), but can be localized.Automating Documentation and Coding: Indian doctors also spend considerable time writing notes or filling forms (though less than Western counterparts due to often having scribes or doing minimal notes in public sector). AI could ease even the existing load. For instance, a voice-to-text system that understands Hindi or Tamil could allow a doctor to verbally record a patient history, which is then summarized in English in the case-sheet by an AI. Given India’s multilingual environment, AI-based real-time translation combined with transcription would be hugely beneficial – a doctor could speak in English but auto-generate discharge instructions in the patient’s native language, bridging communication gaps. Moreover, billing and coding (especially for insurance claims under schemes like Ayushman Bharat) could be expedited by AI reading the procedures and assigning codes. This is mundane work that AI can reliably perform, speeding up reimbursement cycles.Pharmacy and Supply Chain: Hospital pharmacies in India (particularly in large hospitals) experience stock-outs or oversupply due to poor inventory prediction. AI models can analyze prescription patterns and seasonal trends to predict drug consumption and suggest optimal restocking times, reducing waste and shortage. Similarly, AI can optimize staff rosters – a big private hospital could use ML to forecast patient load by day/hour and schedule nurses and doctors accordingly (some Indian hospitals already use simpler statistical tools for this; AI could refine it further).Tele-ICU and Remote Monitoring: While not classical “workflow automation,” the concept of tele-ICUs in India (where specialists in cities remotely monitor ICU patients in smaller hospitals) is growing. AI can augment this by automatically analyzing vital sign trends and alerting the central team to patients needing attention (essentially an AI-driven early warning score). Startups like CloudPhysician in India pair ICU telemonitoring with intelligent alert systems, exemplifying how AI improves workflows by focusing expert attention where needed most. Benchmarks: Innovative Health Systems and Automation Singapore: Singapore’s healthcare system offers a template for smart hospitals. Beyond the earlier examples (readmission risk, HOSPI robots), Singapore has integrated technology at every level. Public hospitals use a national Electronic Health Record (NEHR) that feeds data into AI systems. For instance, Singapore General Hospital reportedly uses AI analytics to predict ICU bed requirements hospital-wide, and Changi General’s command center uses predictive dashboards for emergency admissions. A key success factor in Singapore is strong government backing and a culture of innovation – hospitals there collaborate with tech companies through initiatives like the Centre for Healthcare Assistive Robotics Technology (CHART) to trial new automation like HOSPI. The result is a continually evolving workflow where machines handle more routine tasks and humans can supervise. Indian hospitals could seek similar public-private partnerships (perhaps under the Smart Hospitals initiative by the National Health Authority) to pilot robotics or AI systems, especially in new AIIMS and large state hospitals that aim to be centers of excellence.Scandinavia (Denmark/Sweden): In these countries, the focus has been on system-wide optimization. Denmark’s move to cloud-based imaging across all hospitals in its Capital Region (which includes Copenhagen) illustrates how centralization enables AI (they mention it will allow easier adoption of AI for diagnostics and workflow across the region). Similarly, Sweden has some of the world’s lowest average ER wait times and high efficiency, partly due to embracing digital tools that route patients to the right level of care. Many Swedish primary care clinics use symptom checker AI (like the app KRY) to triage patients before they come in, thus reducing unnecessary doctor visits – this overlaps telemedicine and workflow, but ultimately optimizes clinic load. For hospital ops, Nordic hospitals lean on data – they have high EHR penetration, enabling AI use. One lesson is that data standardization and sharing (while respecting privacy) across facilities in a public system can magnify AI’s impact. India’s state health systems (like in Tamil Nadu or Kerala) could emulate this by creating state-level health data pools to drive AI models for public hospitals (e.g., predicting disease outbreaks, optimizing ambulance distribution).United Kingdom: The NHS, being a huge system, has pockets of innovation in workflow AI. Aside from the appointment AI example, NHS has deployed or trialed AI for hospital bed management (using prediction algorithms to support what they call “operational command centres”). Some NHS Trusts have an AI-based system to bleep (alert) the right on-call teams for deteriorating patients based on vital signs – effectively automating escalation protocols. The NHS also has a vision of “learning health systems” where data from every patient encounter feeds into algorithms to improve care pathways continuously. A concrete benchmark is NHS’s use of AI in admin tasks: they launched a National Clinical Coding Collaboration with AI to automate coding of procedures from text, aiming to save thousands of person-hours. The UK example shows that government funding and central strategy (the NHS AI Lab) can accelerate adoption of workflow AI by providing grants and guidance, something India’s central and state governments could consider (e.g., funding AI pilots in government hospitals that improve service delivery, not just clinical outcomes).Japan: Facing an aging society and clinician shortages, Japan has turned to automation out of necessity. We discussed robots, but also AI chatbots and kiosks are used in some Japanese hospitals to handle initial patient intake (especially in large urban hospitals where many patients come for consultations). For example, some clinics have an AI-driven symptom intake on a tablet; patients input their complaints and the system prints a summary for the doctor along with a preliminary risk assessment (this blends diagnostics and workflow). Japan’s government, under its Society 5.0 initiative, has pushed for “Smart Hospitals” where technologies like AI and IoT are deeply integrated. A lesson here is the importance of interoperability – Japanese efforts highlighted that AI systems must connect with existing hospital information systems seamlessly. For India, where many hospitals use different software (or none at all), building interoperability standards (potentially part of the ABDM blueprint) is critical to allow AI plugins into various workflow steps without starting from scratch each time. Challenges and Limitations in India’s Hospital AI Automation Implementing AI for workflow optimization in India’s healthcare facilities comes with its own set of challenges: Digitization Levels: The foremost barrier is that a large number of Indian hospitals (especially government-run) are not fully digitized. Many processes are still paper-based – from patient registers to inventory logs. AI thrives on digital data; without it, there’s nothing to analyze. Rolling out hospital information systems (HIS) and electronic medical records is a prerequisite. The Ayushman Bharat Digital Mission is pushing in this direction by introducing standardized health record systems. However, the pace is uneven. Private corporate hospitals are ahead in IT adoption, whereas smaller nursing homes and rural hospitals lag far behind. This digital divide means AI solutions might initially benefit the already-better-off tier of hospitals. Bridging this gap will require policy incentives and investments in basic IT infrastructure for healthcare at the grassroots.Data Silos and Integration: Even where digital systems exist, integration is a problem. Different departments might use different software that don’t talk to each other (for example, lab software separate from admission system). An AI model needs aggregated data (e.g., to predict admissions, you want data on local disease incidence, weather, past hospital trends, etc., combined). Indian hospitals need to break down data silos and possibly adopt middleware that can pull data from multiple sources for AI processing. Interoperability standards (like FHIR for health data) are still nascent in deployments. Without unified data, AI predictions might be incomplete or less accurate.Customization to Local Workflow: Hospital workflows vary widely across India. A solution successful in a Singaporean hospital (with, say, a certain nurse-to-patient ratio and process) might not directly map to an Indian public hospital. AI algorithms may need retraining or rule adjustments to account for local realities (for example, patients in India might be more likely to miss appointments due to travel distance or cost factors, which an algorithm must learn as predictors). There’s also the aspect of patient behavior – e.g., in some cultures patients arrive much earlier than appointment time to queue; in others, they walk in without any appointment. AI models must be tailored to these patterns to be effective. This requires local data collection and involvement of hospital staff in designing the AI workflow.Workforce Adaptation and Trust: Just as with diagnostics, staff must trust and know how to use AI in operations. There could be resistance, for instance, from scheduling clerks or administrative staff who fear being made redundant by automation. Change management is crucial – hospitals should train staff to work with the AI systems (e.g., a clerk uses the AI’s prediction but still applies human judgment for special cases). Clear communication that AI is there to assist and reduce drudgery (not to cut jobs) will ease acceptance. Moreover, any AI that directly interacts with patients (like a chatbot) needs careful monitoring initially; if it makes errors in communication, patients will lose confidence quickly.Resource Constraints: Implementing advanced AI solutions can be costly – not only the software licenses or development but also hardware (servers, network) and maintenance. Many Indian public hospitals operate on tight budgets. Without external funding or clear cost-benefit analysis, administrators might be reluctant to invest in AI for workflow, as it’s seen as a nice-to-have rather than essential (unlike hiring a new doctor which has a visible impact). To overcome this, solution providers may need to offer flexible models (e.g., cloud-based solutions with low upfront costs, or pilot programs funded by government innovation grants). Demonstrating quick wins (like the no-show reduction leading to measurable revenue increase in a private hospital, or reduced crowding in OPD improving patient satisfaction in a government hospital) will be important to justify scaling up these tools.Privacy and IT Security: As hospitals use AI that aggregates data, the risk of data breaches or misuse arises. Patient data in India is sensitive, and with the upcoming data protection law, hospitals will be held accountable for safeguarding it. Any AI systems introduced must comply with security standards (encryption, access controls). Also, automated decision systems in a hospital must have fail-safes – for example, if an AI scheduler goes down, there needs to be a manual override to avoid chaos. Ensuring reliability and security of AI platforms is a challenge that tech providers must address upfront to gain the confidence of hospital IT departments. In conclusion, while Indian healthcare could significantly benefit from AI-driven workflow improvements (perhaps even more so than some rich countries, given our larger inefficiencies), the path requires foundational work in digitization, training, and trust-building. The payoff would be smoother hospital operations that can handle the growing patient load more effectively, making care delivery faster and more patient-friendly. With supportive policy (like inclusion of AI solutions in government healthcare schemes) and demonstration projects that showcase success, we can expect increasing adoption of such automation in the coming years.

In the past, the health care sector was ridden with so many challenges not only to the patients but also the providers. However, patients encountered various limitations in diagnostic access due to cost and geographic barriers, resulting in misdiagnosis or delayed diagnoses, because of manual data analysis and generic treatment plans. Besides, getting around confusing medical terminology and being ignorant of their treatment left them annoyed. In contrast, healthcare organisations were developing under the burden of administrative work, information overload, the shortage of qualified staff and the high cost of operations, which restricted accessibility and resources. This is where AI comes in to change the healthcare industry. With the use of AI algorithms that analyze medical datum with higher accuracy, diagnoses are being done earlier and more precisely and individual treatment schemes are being determined which enhance the outcome of a patient. Virtual assistants help to bridge access gaps since they provide basic medical advice and support, while AI-powered tools translate the complex medical information, enabling patients to actively participate in the decision-making process for their care. For providers, AI automates administrative duties, freeing up time for patient care. In analyzing large data, AI provides significant insights to informed decision-making and allocation of resources, the reason why it speeds up the development and discovery of drugs. Also, AI allows forecasting the failures of the equipment thus reducing the downtime and increasing the efficiency. The impact is undeniable. By 2025, the worldwide healthcare AI market is projected to reach $67.5 billion as AI-based tools are already processing billions of medical images. The expected cost savings for the US healthcare system by 2026 is $150 billion annually. This blog talks about AI’s Impact on Healthcare discussing current applications, benefits and future. Real World Examples of AI in the Healthcare Industry As artificial intelligence has been incorporated in the health care sector by many companies, there have been considerable improvements. Here are some examples: Viz.ai In the field of healthcare, every second counts, and delays may lead to death, so Viz.ai allows care teams to respond quicker with innovative AI-powered solutions. It takes a quick response from the care teams, in the case of AI products provided by the company, they can easily detect the issues and the care teams can be informed easily, then providers can discuss the options, provide faster treatment decisions, and it helps to save lives. Buoy Health The symptom and treatment checker Buoy Health, created by a group from Harvard Medical School, is a diagnostic and treatment tool that runs on artificial intelligence algorithms to identify and cure ailments. The process of crafting the personalised care tracks for the management of medical conditions such as digital therapeutics, care communities, and coaching options is unique to this company. Linus Health The company’s brain health mission is guided by the early brain detection system of the Linus Health platform for cognitive assessments. Its proprietary assessment technology DCTclock takes the gold standard pen-and paper clock drawing test for early signs of cognitive impairment and digitizes it, combining the latest developments in neuroscience and AI to analyze more than 50 measurements that reflect the cognitive functions of the patient. PathAI PathAI creates machine learning technology to help pathologists diagnose better. The goals that the company currently seeks to achieve include minimizing error in cancer diagnosis and creating ways for personalized medical therapy. Second, PathAI collaborated with drug developers, including Bristol-Myers Squibb, and with organizations such as the Bill & Melinda Gates Foundation, to use its AI technology in other healthcare sectors. Beth Israel Deaconess Medical Centerx The teaching hospital of Harvard University, Beth Israel Deaconess Medical Center, used AI for diagnosing lethal blood diseases at the early stages. AI-enhanced microscopes are designed by doctors to facilitate scanning of harmful bacterias like E. coli and staphylococcus found in blood samples at a faster rate than manual scanning can. The scientists used 25,000 images of blood samples in training the machines to find bacteria. Next, the machines learned to distinguish and anticipate dangerous bacteria in blood with 95 percent accuracy. Augmedix This organization provides an AI-based medical documentation toolkit for hospitals, health systems, individual physicians, and group practices. It uses natural language processing and automated speech recognition in its products to enhance productivity and patient satisfaction. Cleveland Clinic, Ohio The Cleveland Clinic has shown how AI can be used to provide personalized healthcare plans on an individualized basis, showing the possibilities of AI in improving personalized medicine. Some specific examples of generative AI being used in the healthcare industry.  According to a report on the BCG website, generative AI is projected to grow faster in healthcare than any other industry, with a compound annual growth rate of 85% through 2027, reaching a total market size of $22 billion. The report identified more than 60 use cases for generative AI across the entire medtech value chain, and ranked them based on their impact and speed of implementation to identify the biggest near-term opportunities. The common theme is that all have strong potential to help medtech companies work smarter and faster, ultimately creating value for the companies ambitious enough to apply them.  The global market for generative AI in healthcare reached USD 1.07 billion by 2022 and is expected to grow at a CAGR of 35.14% over the forecast period from 2023 to 2032. Generative AI has immense potential for improving healthcare outcomes, from enhancing medical imaging and patient care to enabling personalized medicine and streamlining administrative tasks. Few examples are: Facilitating Medical Training and Simulation: Generative AI models can be trained on medical images, lab tests and patient data to create realistic simulations for training in the medical field where practice is risk-free. Assisting in Clinical Diagnosis: By using generative AI, large datasets can be analyzed and diseases detected by the information fed into it; this may help in early disease detection leading to personalized treatment. Contributing to Drug Development: Using generative AI, virtual compounds can be created and their properties studied to speed up the drug development process. Automating Administrative Tasks: This entails the creation of artificial medical data, automation of documentation and refining EHRs to make them effective and user friendly. Enhancing Medical Imaging: These generative AI models are able to produce images similar to real ones, which can be beneficial in bettering the medical imaging techniques and diagnostic support. Benefits of AI in Healthcare The benefits of AI in healthcare are numerous and include: Improved Accuracy and Efficiency in Diagnosis and Treatment: AI algorithms allow processing large volumes of data in a very short time and with a high level of accuracy, enabling healthcare providers to diagnose and treat diseases faster. With the help of AI, healthcare providers will be able to react quickly to possible emergencies and prevent tragic states. AI can also assist healthcare practitioners in managing chronic diseases through tracking of the health information of patients over time and suggesting lifestyle changes. Enhanced Patient Outcomes and Personalized Care: AI can use patient data analysis to design patient-specific treatment plans, improving patient outcomes and decreasing the chances of adverse events. AI-driven technologies, in turn, can create customized treatment pathways for managing medical illnesses, provide medical documentation kits, and equip clinical decision support virtual assistants. AI may target those patients who stand a good chance of responding to particular treatments, detect diseases early and accurately, and enhance the efficiency of services by refining estimates of demand. Cost Reduction and Operational Efficiency: It can shed the administrative workload of the professionals of healthcare, reducing the costs and allowing them to devote more time to the care of the patients. AI reduces costs associated with insurance refusals by detecting and preventing the wrong claims before the insurance companies reject the money. AI allows providers to access more patients, particularly those in sparsely populated areas and the underserved, widening the reach of healthcare. AI is projected to cut costs, eliminate mistakes, improve therapies, and to in the long run enhance health results. In a Nutshell, Though issues of data privacy, ethics, and human supervision persist, AI undoubtedly has a limitless capacity for revolutionizing healthcare. It provides an avenue to a better future which will be more accessible, efficient and effective, creating a more healthy environment for both the patient and the provider. Tipstat, a software technology partner, recently worked on a project with MindAI (https://www.mindai.in/mindai) as a platform for digital counselling, personalized for mental health. This partnership is an indication of Tipstat’s proficiency in digital transformation and AI innovation, which displays their potential in the healthcare industry by using their AI product design and implementation skills. The project highlights the possibilities of AI as the first step towards the improvement of mental healthcare delivery and as a support to people concerning their mental health. This partnership points to the practical use of AI in meeting the vital needs of healthcare, which also accentuates the importance of AI to healthcare revolution.

While there are many development companies in the market today, finding the best partner for your business will be harder than you thought. You need a trustworthy and capable company like Tipstat Infotech if you are looking for the best solutions and results! Founded in 2012, our experience and extensive knowledge in the development industry are guaranteed to help you and your business. As a matter of fact, our team has been named as one of the best software developers in India by Clutch. Their recent press release unveiled some of the best local developers locally and we are very proud to be included in this prestigious list.  Clutch, in case you haven’t heard of them before, is an established platform in the heart of Washington, DC, committed to helping small, mid-market, and enterprise businesses identify and connect with the service providers they need to achieve their goals. Our team is still enamored with this recognition. We are truly grateful to Clutch and their team for taking the time to make this award happen. It is a huge privilege for us to be named as a 2022 Clutch leader and we will truly cherish this for a long time. Lastly, here is our CEO, Alok Pandey, to talk about this award: “We would like to thank Clutch for their recognition and appreciation. Clutch is known for its honest feedback system and any form of recognition from them means a lot to us. Thanks a lot.” We would be happy to partner with you for your software development needs! Reach out to us here and we’ll make sure to get back to you ASAP.

We at Tipstat have faced a lot of these challenges with offshore software development over the years and would like to share our insights with clients and companies. What makes a project successful? We can say a project is completed successfully if the project meets these three parameters: 1. Its predicted timeline 2. It’s budget 3. The goal It’s quite simple to write down the various methodologies followed for completing a project. Finding an idea, discussing the various aspects of the idea, modifying it based on related previous experiences, analyzing merits and demerits and surveying the success of the project, calculating the resources and budget of the project, etc are some of the steps put forward by successful project management experts. But still, the rate of project failures is surprisingly getting high. Let’s check out why most IT projects get delayed, canceled, or go over budget. Why do IT projects fail? According to The third global survey on the current state of project management, The poor estimation during the project planning is the largest contributor(32%) to the project failure.  (Source:https://www.pwc.com.tr/en/publications/arastirmalar/pages/pwc-global-project-management-report-small.pdf) Even though this is an era of astounding technologies, nobody has invented a common solution to complete a project successfully. The reasons for IT project failures can depend upon client-related issues, project team-related issues. Let’s discuss them in detail. Client-related Issues The whole future of the successful completion of a project depends on the vision of the client. If the client has made a perfect road map that can be flexible moving forward based on real-time issues, then the project has a good chance of getting completed successfully. The problem arises when: 1. The scope of the project exceeds the budget: The idea of the project gets expanded when the development starts and with such expansion increases the cost. Other reasons can include sudden economic changes (war, resource price hike or unavailability, etc), loss faced in clients other projects, unavailability of funds from promised sources, etc. 2. Lack of interest: Some projects show their true colors in the middle of the development. The client might have felt that the idea is easy but due to the lack of proper research, such clients will re-evaluate that the complexity of the procedure of their idea implementation is high and that the project may not have the expected potential if finished. 3. Another opportunity: If the client gets a different offer that can give them much better results, then there are chances that they can either pause or shut down the current project. 4. Partnership fights: If the client has multiple partners and they get into fights that result in goodbyes can end the future of the project as well. Project Team related issues 1. Lack of a proper team:  If the assigned project is complicated and there is no one in the team experienced enough to handle it, then the project can miserably succumb to non-existence. Also, if the same team is handling multiple projects from different clients, long delays or failures can happen. 2. Lack of proper communication: This is one of the major issues faced in all projects. If the client fails to convey the exact idea of the end product that they are expecting, then the project team will proceed on a road that will take them to nowhere. Similarly, if the internal communication between the sales team, development team, and testing team is not in the same frequency, then the chances for project failure increases. 3. Timelines, Deadlines: There is a saying that “It’s always easy to begin something but difficult to complete it”. For the successful completion of an IT project, it’s important to schedule proper timelines. There will always be a deadline marked by the client. Due to the unprofessional approach of the team, deadlines can be delayed. 4. Changes in the project team: Even though a project is handled by a team of 4-6 members, in most cases there will be a single brain that storms out the best ideas and accomplishments. If the team members who are more experienced and talented are removed from the project, then the expected deadlines cannot be met. 5. Lack of proper tools, resources: If the project team is not using standard scientific methodologies in planning, preparing, implementing, and auditing the project, then the chances for project delay, deviation from the core idea, and failure can occur. 6. Testing vs Development: There is always a war between the development team and the testing team. In most cases, this competition is good for the refinement of the project. But if the war is unhealthy, then the project gets delayed or canceled. User- related issues The users or customers are the ultimate group of people who decide the success of any project. If the project can’t convey its true purpose, then it is considered useless even if it gets completed. When a project team works independently in a closed environment, they lose open communication with users and fail to get feedback from them. So at different levels, the project team invests time resources, and money on things that are not needed by the end-user. The user may not even use the product due to the user interface complexity. Resolution According to the Project Management Institute(PMI), “There is no single method or organizational structure that can be used to manage projects to success”. Let’s see some of the most common methods used to resolve the issues that can cause project failures. 1. Keeping clients in the loop from start to end: This is very important. There should be frequent communication between the client and the project team. The client should be informed about each step taken in the project cycle. Similarly, the project team should set up presentations and detailed reports after the completion of each milestone. 2. Proper Project Planning: The bigger the project, the smarter the plan should be. There should be a well-documented guideline or project plan to follow. This guideline has to be created in the presence of the client(in most cases the client will provide a guideline, the team can modify that based on their workflow). The document should be followed in all cases. The selection of proper members in the team is also important. The project team should contain people who have prior experience and deep insight with similar projects.  3. Adapting to the real-time changes: According to THE HARVEY NASH / KPMG CIO SURVEY 2017, 64% CIO’s say that the political, business and economic environment is becoming more unpredictable (Source:https://www.hnkpmgciosurvey.com/pdf/2017-CIO-Survey-2017-infographic.pdf). Hence the project team should always be willing to face unpredictable problems in the project roadmap. Identifying real-time issues and solving them without exceeding the deadline is the key part.  4. Communication is the key: According to the third global survey on the current state of project management, Implementing efficient and effective communication strategies positively affected projects quality, scope, business benefits, performance levels, etc  (Source: https://www.pwc.com.tr/en/publications/arastirmalar/pages/pwc-global-project-management-report-small.pdf ).  Studies show that projects with healthy communications are having more success rates. The client-project team communication and interpersonal communication inside the team should be effective. 5. User is the king:  Even if the project met all the parameters that are essential for its success, it can miserably fail due to one factor: customer satisfaction. There should be a bridge between users and the project team so that they can test the real-time usage and functioning of the project with users. This is the reason why famous companies release a beta version before launching the product. Hence we can say that the project management process requires a good investment in planning, strict feasibility checks using effective tools, efficient communication, an experienced team, vigorous testing at each level,  and active user interaction sessions.  References: 1. https://www.pmi.org/learning/library/seven-causes-project-failure-initiate-recovery-7195 2. https://www.cio.com/article/3211485/why-it-projects-still-fail.html 3. https://www.atspoke.com/blog/it/reasons-for-it-project-failure/ 4. https://www.techrepublic.com/article/6-reasons-why-your-it-project-will-fail/ 5. https://www.objectstyle.com/agile/software-projects-failure-statistics-and-reasons 6. https://www.pwc.com.tr/en/publications/arastirmalar/pages/pwc-global-project-management-report-small.pdf 7. https://www.hnkpmgciosurvey.com/press-release/ 8. https://www.hnkpmgciosurvey.com/pdf/2017-CIO-Survey-2017-infographic.pdf 9.https://ruor.uottawa.ca/bitstream/10393/12988/1/El_Emam_Khaled_2008_A_replicated_survey_of_IT_software.pdf 10. https://www.iqvis.com/blog/why-it-projects-fail-top-8-reasons-explained-here/

What are the Issues with Offshore Software Development? 1. Cultural Differences It has been scientifically proven that people from different regions react to the same situations in different ways. And the reason for this is the cultural differences. For example, Asians can’t tolerate direct criticism even if they are wrong. The client has to find a convenient way to present what went wrong. But the European culture is comparatively open to criticism. The cultural differences between Asian countries and European countries are entirely different. In a research study based on the behavior of Indian vendors and german clients, it was found that the Indians are weak in saying “no” to several situations. It further explains that offshore developers in India have a tendency to offer a service attitude due to which it is difficult for them to say no to something or to tell a piece of bad news. This can be bad for software development. The cultural differences impact interactions, communication, interpretation, understanding, productivity, comfort, and commitment. 2. Expecting the Impossible If your client is coming from a different working domain other than software (like the wooden industry that needs an e-commerce site for their finished products, logistics, travel, and tourism, real estate, etc) industry, then their understanding of the software workflow may not be identical with yours. The client logic about the whole process can be similar to “press the switch and the lights are on!”. If your client is putting forward an idea that is close to impossible, then the project can get stuck in the early phase itself. 3. The ‘Long Distance Relationship’ Issues As you go out of your country or continent to find out the best third party partner for completing your project, there is another factor that you should be worried about. The time zone! Let’s talk about the USA and India. If you are based in the USA which is almost 9 hours and 30 minutes behind India, then your active working time has to be rescheduled for the betterment of the project. In case the Indian offshore software development company provides you a sample data based on the instructions mentioned, and if the delivery time is between 9 am to 6 pm (normal Indian office hours), then you would be probably sleeping (11 pm – 8 am)! However, if the sample provided needs to be modified, you have to analyze it and should provide detailed feedback. Again, by the time you provide feedback, your third party partner firm would have logged out. So it can take roughly 12-24 hours to complete a communication cycle unless one of the two becomes flexible and agrees to follow the same time format. 4. Lack of Requirement Clarity  This is a major issue that needs complete attention. If the project is yours, then it’s quite easy to describe each of the project goals and requirements without any confusions or hesitations. But if the project is for your client, who hasn’t provided a perfect description of the project, then the chances for the project to become close to worthless is high. The first thing to make sure of is, whether you have the precise knowledge of the requirement. The second thing is you should share the requirement with detailed instructions to the software outsourcing company. If you fail in any of this, then the project can fail miserably. 5. The Cost Issue The whole idea of software outsourcing was mainly because of the cost advantage. But what happens if the cost surpasses the estimated limit? Yes, it’s a big problem! Normally, offshore software development works are calculated in a rate per hour method. But the lack of experience in the stream increases errors and software testing time unpredictably. If the actual work can be done in under an hour, the errors can delay the work for days or even weeks! And the cost per hour can exceed the budget limit. What are the Solutions?  1. Overcome Cultural Differences This is one of the most important topics to deal with while choosing an offshore software development company. Especially, if the project is handled by a group of people from Europe as well as Asia, the difference in culture will be evident. The Asian culture tends more to work based on well-documented guidelines whereas Europeans are more into logic-based documentation. Effective communication is a key factor in such heterogeneous groups. Adjusting meetings based on team timezones is also important. However the most important of all is to understand the common goal of the project. One has to make sure that they as well as the team members are on the same page of project workflow. Setting up short milestones in the road map and a detailed evaluation of the milestones after their completion can be very helpful. Sometimes offline meetings can help reduce confusion in understanding the goal of the project. 2. Does this Project Need Outsourcing? This should be the first question in your mind while analyzing the project in your hand. The project analysis should be based on variables that can influence the completion and profit of the project. The size of the project, the average time needed for the completion, nature of the project, various costs related to the project, etc are some of the factors that have to be taken into account while calculating the scope of the project. Once you cross-check the detailed report based on all these analyses, you will get a better picture of whether you need an offshore development company or not. 3. Choosing the Most Suitable Offshore Development Company Once you have confirmed the need for outsourcing by analyzing the scope of the project, the next step to do is to locate the most suitable third party firm. There is nothing called the best firm. Based on the nature of the project, you have to choose the most suitable outsourcing partner. Let’s discuss some factors that can help identify the choice. Value Over Cost Consider two firms namely A and B. Outsourcing company A bids with $100 cost and B bids with $50. Now the easy way is to pick B over A based on cost. But the right way is to identify the value of the A firm and B firm by running some background checks. Technology can help you to locate the previous works done by the firms. If they have showcased successful projects similar to the one you have in your hand, then that firm is the best one for you due to the previous experience. Also, you can try to get connected with their previous clients who can share their experiences with you. If the clients are available, you will be able to get details on their working nature, customer satisfaction, deadline keeping, and reporting. Agreements This is the most important part of the partnership. Since the project idea and related resources are of high value, having a legally bound agreement is a must. Agreements make sure that you are dealing with a reputable outsourcing company and allows you to relax that your project is in safe hands. Ownership right agreement, Contract/Sow, Termination rights agreements, NDA(non-disclosure agreements), etc are some of the essential agreements when you deal with an offshore enterprise. Do you want to outsource your software requirements? Connect with us today and build a reliable team of offshore developers easily! 4. Proper Project Management Another important aspect to avoid outsourcing issues is proper project management. After preparing a full-proof SOW, the next thing should be preparing developer-friendly guidelines for the easy understanding of the project. Guidelines can be made using powerpoints, documents with diagrams, data flow charts, etc. Such detailed documentation can help the developer to understand the project closer. You can fix timelines by breaking the big project into small modules. Predicting a timeline for the complete project may be difficult. But it is easy to determine the timeline of the completion of each module. There should be project meetings with the project handling team at regular intervals, preferably after the successful completion of each module.  Advantages of Offshoring to India vs other countries Being first in the countries with most software companies that have ISO 2000 certification and second to the US in software exports, India undoubtedly is on the top list of countries favorable for offshoring. The 2016 A.T.Kearney Global Services Location survey shows that India is the first choice for BPO. Let’s discuss the advantages of India over other countries in offshoring. 1. Cost Indian offshore developers work for an average cost of $10 – $20/hr which reduces the complete project cost to a significant level. The high competition among Indian companies also creates a cost-effective platform for offshoring. 2. English Language Advantage Thanks to the 200 years of British rule in India, the convent school culture taught Indians good English which makes them the best choice for offshoring in comparison to other offshore competing countries like Ukraine, Malaysia, and China. 3. Quality Indian offshore developers are highly sought after in the software industry. According to NASSCOM, most of the FORTUNE 500 companies around the globe use Indian built software that shows the level of quality standards. Also, the highly qualified and experienced experts make no compromise in software build quality.  4. Support, Maintenance, and meeting Deadlines Indian offshore development companies offer extensive support to their software. Some companies offer a lifetime maintenance assurance with 24*7 support lines. Indian companies are well known for delivering software products before deadlines. ISO and SEI CMM based work standards, timezone flexibility, commitment, stable and calm political environment, IT-friendly laws and policies, etc are some of the other significant advantages. Based on a study by Evans Data Corp in 2013, there were approximately 2.7 Million software engineers in India. In short, researchers point out that India will overtake the US by 2024 to become the country with the largest software developer population. 5. Cost Analysis (the US vs India) Australia is considered as the top paid country in the software industry around the world, and the US comes second in the list, based on Time Doctor, DOU. Since India comes 9th on the list, it shows the relevance of the increase in the offshoring software business. According to various job sites, The average annual salary for a software developer in the US is greater than 100,000 USD whereas the average annual salary for an Indian software developer comes under 8,000 USD. Hence, by comparison, it can be seen that you have to spend an amount of 12-13 times higher for a US developer compared to an Indian developer. References: https://www.researchgate.net/publication/247767126_The_Evolution_of_Offshore_Outsourcing_in_Indiahttps://www.consultancy.uk/news/3169/the-top-40-countries-for-business-process-outsourcinghttps://medium.com/existek/types-of-it-outsourcing-and-types-of-contracts-in-outsourced-software-projects-management-c7b18d7d63eahttps://fullscale.io/common-offshore-software-development-challenges/https://idapgroup.com/blog/offshore-software-development/https://www.weblineindia.com/blog/offshoring-software-development-avoiding-risks/https://www.ishir.com/blog/232/offshore-software-product-development-risks-and-best-practices.htmimmihelp.com/indian-english-american-english-language-dictionary/https://www.yourteaminindia.com/blog/cost-benefit-analysis-of-outsourcing-usa-vs-india/https://www.thinqloud.com/benefits-of-offshoring-software-development-and-technology-support-to-india/https://www.daxx.com/blog/development-trends/number-software-developers-worldhttps://www.payscale.com/research/IN/Job=Software_Engineer/Salaryhttps://www.glassdoor.co.in/Salaries/india-software-developer-salary-SRCH_IL.0,5_IN115_KO6,24.htmhttps://relevant.software/blog/6-best-practices-to-overcome-cultural-differences-in-offshore-software-development/

Meet the Robot Chemist Three years of experimental research by a team under Professor Andy Cooper, University of Liverpool, has finally come up with an astounding invention: A Robot lab assistant! The core idea behind the research was to create a machine that can move on the lab floor and perform experiments just like a human lab assistant. The robot has to be custom programmed based on the laboratory it is installed in. But once completed, it can handle the assigned tasks for 22 hours a day and 365 days of the year unless an unexpected maintenance is required. It takes approximately 2 hours for a full charge. Benjamin Burger a Ph.D. student who led the scientists in a trial experiment said that the work covered by the “new lab assistant” is 1000 times faster than the average humans which is remarkable. Andy Cooper on the other hand has given more emphasis on his vision to free the human brains from the repetitive and boring experiments in the research labs. The Machine   The robot components were from KUKA(is a German manufacturer of industrial robots and solutions for factory automation). For the experiment, they used a mobile robot-hand, mounted upon a mobile base station. The robot arm could carry a weight of up to 14Kg and can stretch up to 820mm. The whole system weighed 430Kg and the speed of the robot was limited to 0.5m/s due to security reasons. The robot hand was equipped with a multipurpose gripper capable of handling sensitive glass vials, cartridges, and sample racks.  The robot automatically charges its battery whenever the charge drains to a 25% threshold in between the works. The robot was idle for 32% time mainly because the gas chromatograph analysis was a time-consuming process. The AI guidance used in the robot was based on the Bayesian optimization algorithm. Even though the robot was equipped with basic parameters needed for experimenting, it used the algorithm for deciding 10 different experiment variables. The machine navigates labs using LIDAR, the same laser-based technology found in self-driving cars which allows it to work in dark conditions also. The Successful Experiment  The experiment given to the robot was to develop photocatalysts, which are materials used to extract Hydrogen from water using light. This area of research is crucial for green energy production. Unlike other machines that are programmed to perform a set of pre-recorded instructions, this robot loaded samples and mixed it in fragile glass vials, exposed them to light, and conducted gas chromatography analysis. The major turning point in the experiment was Its adaptability to the workflow just like a human lab assistant.  In an Eight-day period, the robot conducted 688 experiments, made 319 movements between various stations, and covered a total of 2.17Km during the whole experiment. Based on the experiment reports, it was found that the robot did an amazing job that could’ve taken several months for a human. The limitations of a human lab assistant To show the advantages of machine arms, let’s have a look at how a human lab assistant performs the lab chores. A normal lab assistant can be either a full-time lab assistant/technician or can be an aspiring Ph.D. student who does his/her part-time job while working with his/her thesis. As per the normal human working hours, they may work for 9-12 hours a day or more, depending upon the nature of the research. Considering the possible break hours for coffee, snacks, chit-chats, lunch, smoke/bathroom intervals, etc, the working hours shrink again.  Also, we have to consider the emotional issues that can make the human mind cloudy and thus affect their judgment in vital scientific hypotheses and conclusions. And no humans can work consistently for more than 20 hours a day, 7 days a week, 365 days a year. That’s where Laboratory Robotics becomes significant. A helping robotic hand There are several areas in a lab research facility where a robot becomes handy. Those areas include handling extremely dangerous chemicals that can instantly kill or cause high damage to humans, biohazard conditions, high radiation-emitting experiments, laboratories that are constructed underground or places with low-level oxygen, and other health-supporting supplies, etc. Is this robot a replacement for humans? “Absolutely No!”, says Professor Andy Cooper, the head of the robot research department. According to him, the whole idea behind this robot assistant is to save the most valuable parameter in the scientific world, which is time. Cooper further assures by saying that neither the robot invents or designs the experiments nor does it come up with any hypothesis. It acts as a tool to perform all the tedious, boring, and repetitive works in the lab as part of an experiment in which human brains are not to be wasted.   What’s Next? Cooper with his company named Mobotix is planning to commercialize this revolutionary work in the coming months. His idea is to create machines based on different criteria, Say, a robot researcher, a robot technician, a robot scientist, etc. The cost range also varies with the capabilities of the machine. The basic hardware can cost anywhere between $125,000 and $150,000!

The collective research done by Facebook’s AI research team(known as FAIR) and NYU Langone health experts has come up with some interesting news on the field of Magnetic Resonance Imaging or MRI scanning. According to their research, an AI-based technique called FastMRI can dispense output data four times faster than the normal MRI method. The usual MRI procedure The Magnetic Resonance Imaging technique allows doctors to identify various issues related to the spinal cord, brain, neck, and can identify problems in joints, chest, abdomen, blood vessels, etc. The duration of this process can be anywhere between 45 minutes to 1 hour depending on the type and nature of the MRI scan. It is generally an unpleasant situation for any patient when their doctor asks to perform an MRI. Especially for the scans like the head or brain, the patient almost feels like they are in some serious jeopardy. Patients have to lie down on a table that slides into a giant tube for performing MRI. Patients have to remove all of the metal accessories(hairpin, Zippers, Jewelry, Body piercings, etc) from their bodies including removable dental implants. When the procedure starts, the machine will make loud buzzing noises. Even though the patient is allowed to use earplugs, the sound coming from the giant machine can be frightening. The magnetic field strength generated in the room will be more than 20 times that of earth’s magnetic field. Also, the patients are asked to hold their breath for 20-30 seconds as part of the procedure. While considering all these situations, It is quite normal for patients to feel anxious and nervous during the procedure. Especially if the patient is a kid or a claustrophobic, MRI scans can be very difficult to perform. What is FastMRI The Facebook AI research team and NYU Langone Health, started this program two years before to speed up the existing MRI scan time. Their research was aimed at finding a technology that can reduce the duration of the current MRI Scans. They have trained the AI called FastMRI with a large dataset of MRI scan results and possible patterns. The AI, with the large database, can come up with the scan result much faster than the ordinary scan. Based on the study, the scan reports of a traditional MRI which took one hour were analyzed by radiologists who performed the same analysis with the results of FastMRI. And they found out that both scan reports returned with identical results. What are the benefits of FastMRI The medical world is excited to see the promising results of FastMRI due to the tremendous achievements that can follow with its implementation. FastMRI setup is important where the ratio of MRI scanning machine and the patient count is unmatched. Considering the duration of the current MRI system, it is quite difficult to complete the scan of a number of patients using one machine. The latest FastMRI needs four times fewer data than the existing one to generate scan results. This means, patients suffering from anxiety, claustrophobia, or child patients don’t have to spend a long panic time in the scan room. And the productivity of a single machine can be increased multiple times allowing the service to be reached to more people in need at the right time. It also helps doctors to perform a quick scan on emergency patients suffering from situations like strokes or breakdown. Also, FastMRI can be a good replacement choice over CT scans and X-rays for some typical cases.

Supercomputers are probably the most reliable research tools for scientists and researchers. They play a very significant role in the field of computational science and a wide range of computationally intensive tasks and jobs such as in quantum mechanics, climate research, weather forecasting, molecular modeling, physical simulations, properties of chemical compounds, macro and micro molecule analysis, aerodynamics, rocket science, nuclear reactions, etc. Faster the supercomputer faster and accurate is the computational ability for research. Engineers and scientists incessantly develop better supercomputers with the advancement of time. Hence annually the supercomputers from all around the world are ranked according to their speed. This time Japanese supercomputer ‘Fugaku’ which is at the RIKEN Centre for Computational Science in Kobe has topped the list. It is a successor to the ‘K computer’ which topped the list in 2011. Fugaku will be fully functional from 2021. This supercomputer has been mended with the Fujitsu A64FX microprocessor and the CPU has the processor architecture based on ARM version 8.2A which adopts the scalable vector extensions for supercomputers. Fugaku was aimed to be 100 times more powerful than its predecessor the ‘K computer’. It has been recorded with a speed of 415.5 petaflops in the TOP500 HPL results which is 2.8 times faster as compared to its nearest competitor ‘Summit’ by IBM. Fugaku has also topped the list of other ranking systems like the Graph 500, HPL-AI and HPCG where the supercomputers are tested on different workloads. This is the first time that any supercomputer has topped all the four ranking systems which makes it significant reliability for future purposes. The cost of this supercomputer was estimated to be around 1 billion USD which is around 4 times more than that of its next competitor ‘Summit’. This humungous cost on the project has caused a significant controversy from many experts. According to the New York Times, similar featured exascale supercomputers will be developed in the near future with a very low cost as compared to Fugaku. There has also been heavy criticism of the government as some speculate that the government is spending way too much on this project just to be primal on the list amidst the pandemic. Recently Fugaku is being used in the research for the drugs of Covid-19, diagnostics, and simulation of the spread of the coronavirus. It is also being used to track and improve the effectiveness of the Japanese app used for contact tracing in case of contamination. According to the Japan Times, in the latest research, the supercomputer was used to conduct molecule level simulations related to the drug for the coronavirus. A simulation on 2,128 existing drugs was made and picked dozens of other drugs that could bond easily to the proteins. This simulation was run for 10 long days. The results were quite accurate as 12 of the drugs detected by it were already undergoing clinical trials overseas. This research exalted the hopes of scientists for a remedy of the virus. The expert team will continue their research using Fugaku and they have also announced that they will negotiate with the potential drug patent holders so that clinical trials to develop a possible drug for the virus can be carried out. This will allow starting early treatment of the infected people. According to the experts, the supercomputer will also be likely effective to predict and study earthquakes in the future. Japan has a bad history of earthquakes since the country lies above the junction of many continental plates as well as the oceanic plates surrounded by volcanoes. Fugaku can detect chances of earthquakes which will allow the government and the locals to follow an escape plan from the natural disasters.