#IBMWatson
Explore tagged Tumblr posts
Text
Top 5 AI Certifications That Are Actually Worth It in 2025
Published by Prism HRC ā Leading IT Recruitment Agency in Mumbai
Letās face it, in 2025, AI is not "nice to know." Itās everywhere. From chatbots and content marketing to finance and medicine, artificial intelligence is the force working behind the scenes. That also means employers are actively searching for professionals who understand AI or at least know how to work alongside it.
But with countless online courses out there, itās tough to know which certifications actually carry weight. Which ones make your resume stand out to real hiring managers and recruiters?
Weāve curated the top five AI certifications that are genuinely worth your time, effort, and investment in 2025, whether youāre a fresher, a seasoned techie, or someone switching careers.

Google Professional Machine Learning Engineer
Why itās worth it: This certification shows that you can design, develop, and deploy machine learning models on Google Cloud. Itās widely respected in the industry, especially if youāre eyeing cloud-based AI roles.
Who itās for: Mid-level professionals, data scientists, ML engineers
What you'll learn:
Defining machine learning problems
Feature engineering
Model architecture and deployment
Tools like Vertex AI, BigQuery, and TensorFlow
Bonus tip: Just having Googleās name on your resume adds major credibility, especially if you're applying to MNCs or product companies.
IBM Applied AI Professional Certificate (via Coursera)
Why itās worth it: This course is one of the most beginner-friendly yet hands-on AI certifications out there. It teaches you practical tools and includes real-world projects you can add to your portfolio.
Who itās for: Freshers, career changers, and even non-programmers curious about AI
What you'll learn:
Foundations of AI
Python programming for AI
IBM Watson tools and services
How to build chatbots and deploy AI applications
Pro tip: The included labs and projects are great for showcasing your work on LinkedIn or GitHub.
Microsoft Certified: Azure AI Fundamentals
Why itās worth it: A solid starting point for anyone looking to understand AI through the lens of Microsoftās Azure platform. This course makes complex AI ideas approachable without diving into deep code.
Who itās for: Newcomers, business analysts, marketers, and non-tech professionals exploring a switch to AI
What you'll learn:
Core machine learning and AI principles
Natural language processing, computer vision
Responsible AI practices
Use cases and tools in Azure
Why it stands out: If youāre applying to companies already using Microsoft tools, this certification puts you ahead of the pack.
Stanford Online: Machine Learning Specialization by Andrew Ng (on Coursera)
Why itās worth it: Andrew Ng is a well-known name in the AI world, and his course has helped millions break into machine learning. The 2025 version is updated, relevant, and perfect for serious learners who want a deep understanding.
Who itās for: Developers, tech enthusiasts, aspiring machine learning engineers
What youāll learn:
Supervised learning and neural networks
Bias-variance tradeoff
Decision trees
Model evaluation and tuning
What makes it special: This isnāt just a theory-heavy course. It helps you understand how machine learning actually works, and that knowledge is rare and respected.

DeepLearning.AIās Generative AI with LLMs Specialization
Why itās worth it: Letās be honest, generative AI is everywhere right now. Whether youāre playing with ChatGPT or building AI tools at work, this course puts you in sync with the future.
Who itās for: Developers, content creators, product managers, and tech professionals working with AI APIs
What youāll learn:
Prompt engineering strategies
How large language models function
Fine-tuning LLMs
Building ethically sound GenAI applications
Hot tip: If you're interviewing for product, content, or R&D roles related to AI, this certification will make you stand out.
Before you go
Letās cut through the noise. There are tons of AI courses out there, but only a few actually help you grow. These five certifications offer real skills, portfolio projects, and recruiter-approved credibility.
If youāre planning to enter AI, grow in your current role, or shift from another domain, one of these certifications could be the best decision you make in 2025.
Still unsure which AI path is right for your career?
Prism HRC can help you make the smart move. We match skilled talent with companies hiring in AI, data, and cloud, and we know exactly what certifications employers are asking for right now.
Based in Gorai-2, Borivali West, Mumbai Website: www.prismhrc.com Instagram: @jobssimplified LinkedIn: Prism HRC
#AIcertifications#machinelearning#BCAjobs#techcareers2025#upskill2025#learnAI#BestITRecruitmentAgencyinMumbai#AIforbeginners#AIjobsindia#careertransitiontech#generativeAI#LLMcertifications#microsoftazure#ibmwatson
0 notes
Text
š Discover the Best No-Code AI Tools for 2024! š
Dive into our latest blog to explore how no-code AI tools like Akkio, ChatGPT, Canva, and more are revolutionizing the tech world. Perfect for businesses, designers, and hobbyists looking to harness the power of AI without writing a single line of code.
š Find out which tool is right for you! š Boost your productivity and creativity! š± Plus, learn how our expert team can create high-quality, user-friendly mobile apps tailored to your unique needs. Offering top-notch iOS and Android app development services.
š Read the full blog here: https://cizotech.com/best-no-code-ai-tools-for-2024-a-comprehensive-guide/
#NoCode#AI#TechTrends2024#MobileAppDevelopment#iOS#Android#Innovation#TechBlog#Akkio#ChatGPT#Canva#Adobe#AmazonSageMaker#IBMWatson#GoogleAIPlatform#Lobe#AnthropicClaude#Prevision
0 notes
Text
Complete Guide to AI model risk management Framework

AI Model Risk Management involves detecting, mitigating, and addressing AI threats. It emphasizes formal AI risk management frameworks and includes tools, techniques, and principles.
Artificial intelligence risk management aims to minimize its drawbacks and maximize its benefits.
AI risks management Governance of AI includes risk management. AI governance safeguards AI tools and systems from harm.
Artificial intelligence risk management is part of AI governance. AI model risk management targets vulnerabilities and threats to protect AI systems. To ensure safety, fairness, and human rights, AI governance sets guidelines, regulations, and standards for research, development, and application. IBM Consulting can integrate appropriate AI governance into your business.
The Importance Of AI Risk Management AI adoption has increased across industries in recent years. According to McKinsey, 72% of organizations employ AI, up 17% from 2023. Organizations pursue AIās benefits innovation, efficiency, and productivity but donāt always address its risks privacy, security, and ethical and legal challenges.
Leaders know this problem. According to an IBM Institute for Business Value (IBM IBV) survey, 96% of CEOs think generative AI increases security risks. Meanwhile, the IBM IBV showed that 24% of generative AI projects are secure.
AI model risk management can help organizations maximize AI systemsā potential without compromising ethics or security.
Artificial Intelligence Risk Management Like other security risks, AI risk measures how likely and damaging an AI related attack is to effect an organisation. Each AI model and use case has various dangers, but four categories exist:
Data threats Simulate dangers The operational hazards Ethics and law dangers AI systems and organisations can suffer financial losses, reputational damage, regulatory penalties, public confidence erosion, and data breaches if these risks are not addressed properly. Data threats
Data sets used by AI systems may be tampered with, breached, biased, or attacked. From AI creation to training and deployment, organizations may reduce these risks by ensuring data integrity, security, and availability.
Common data threats Security: AI systemsā biggest and most important challenge is data security. Threat actors can corrupt AI data sets, causing unauthorized access, data loss, and confidentiality issues for organizations. AI systems manage sensitive personal data, which can lead to privacy breaches and regulatory and legal difficulties for organisation. Training data determines AI model risk management reliability. Data distortion can cause false positives, inaccurate outputs, and poor decision making. Simulate dangers Threat actors may steal, reverse engineer, or manipulate AI models. AI modelsā architecture, weights, and parameters the key components that determine their behavior and performance can be tampered with by attackers.
AI Model Risk Management Some frequent model risks Input data manipulation: Adversarial attacks trick AI systems into producing inaccurate predictions or classifications. For instance, attackers may give AI algorithms hostile instances to distort or interfere with decision making. PROMPT injections attack huge language models. Hackers trick generative AI systems into disclosing sensitive data, spreading falsehoods, or worse. Even simple cue injections can cause AI chatbots like ChatGPT break system rules and utter stuff. Analyzing complex AI models can be difficult, making it hard for users to understand their decisions. Lack of transparency hinders bias identification and accountability and erodes AI system and provider trust. Threat actors target AI systems during development, deployment, and maintenance via supply chain attacks. Attackers could exploit weaknesses in third party AI development components to breach data or get unauthorized access. AI And Machine Learning In Risk Management
AI model risk management are not magic, but rather complex code and machine learning algorithms. Like other technology, they have operational hazards. If ignored, these risks can cause system failures and security vulnerabilities that threat actors can exploit.
Some frequent operational risks Model drift, where data or data point associations alter, can affect AI model performance. Over time, a fraud detection model may grow inaccurate and miss fraudulent transactions. Sustainability concerns: AI systems are difficult and need scale and assistance. Sustainability issues can make maintaining and updating these systems difficult, resulting in inconsistent performance, higher operating costs, and energy use. AI system integration with IT infrastructure is difficult and resource intensive. Data silos, system interoperability, and compatibility plague organizations. By increasing cyberthreatsā attack surface, AI systems can develop new vulnerabilities. Absence of accountability: AI systems are new, thus many companies lack suitable corporate governance mechanisms. AI systems are generally unsupervised. Only 18% of organizations have a council or board that can make responsible AI governance decisions, according to McKinsey. AI Risk Management Certification Ethics and law dangers When creating and implementing AI systems, organizations risk privacy violations and biased results if safety and ethics are not prioritized. Biased recruiting data may promote gender or racial stereotypes and develop AI models that favors particular demographic groupings.
Typical ethical and legal risks Lack of transparency: Organizations that refuse to disclose their AI systems risk losing public trust.
Not complying with government regulations: The GDPR and sector specific standards can result in costly fines and punitive consequences.
AI biases: Training data can prejudice AI systems, resulting in biased hiring decisions and unequal financial services access.
Privacy, autonomy, and human rights issues might arise from AI judgements. When handled poorly, these issues can damage an organizationās reputation and public trust. Without explainability, AI systemsā decisions are hard to understand and defend.
Unexplainability can destroy trust, reputation, and legal issues. A CEO not understanding where their LLM receives training data might lead to poor headlines or regulatory problems.
Artificial Intelligence Risk Management Framework Many organizations use AI Model Risk Management frameworks to handle risks across the AI lifecycle.
These guidelines are playbooks that explain an organizationās AI rules, procedures, roles, and duties. Organisations can build, deploy, and operate AI systems using AI risk management frameworks to minimise risks, uphold ethics, and comply with regulations.
AI risk management frameworks A NIST AI Risk Management Framework EU AI ACT ISO/IEC norms AI executive directive from the US AI RMF by NIST AI risk was organized by NISTās AI Risk Management Framework (AI RMF) in January 2023. NIST AI RMF has created AI Model Risk Management standards since then. AI Risk Management Software AI RMF helps organizations design, develop, implement, and employ AI systems to control risks and encourage trustworthy, responsible AI practices. The voluntary AI RMF, developed with the public and private sectors, applies to any company, industry, or area.
Two sections make up the framework. Part 1 covers trustworthy AI system threats and traits. AI RMF Core Part 2 lists four functions to help organizations manage AI system risks:
Create an AI Model Risk Management culture in your organisation. Map: Business-specific AI threats Evaluate AI risks Handle mapped and assessed risks The EU AI Act AI development and use in the EU are regulated by the EU AI Act. The act regulates AI systems based on their risks to human health, safety, and rights. The act also regulates building, training, and deploying general-purpose AI models like ChatGPT and Google Gemini. AI Risk Management ISO/IEC Norms AI Model Risk Management standards are available from ISO and IEC.
In AI risk management, ISO/IEC standards emphasize openness, accountability, and ethics. They also provide actionable AI model risk management principles from design and development to deployment and operation.
AI executive directive from the US At the end of 2023, the Biden administration issued an executive order on AI security. This comprehensive directive establishes new AI technology risk management criteria, however it is not a risk management framework.
Trustworthy, transparent, explainable, and accountable AI is one of the executive orderās main concerns. The executive order created a precedent for private sector AI risk management.
How AI risk management aids businesses Despite varying from organisation to organisation, AI risk management can give certain common basic benefits when implemented correctly.
Better security Organizations may improve cybersecurity and AI security using AI risk management. Enterprises can discover AI lifecycle risks and weaknesses by undertaking frequent risk assessments and audits. Their risk mitigation techniques can be implemented after these assessments.
This method may incorporate data security and model robustness improvements. Institutional changes like ethical rules and access controls may be needed. Organisation can reduce data breaches and cyberattacks by taking a proactive strategy to threat detection and response.
Better judgement AI risk management can also improve group decision making. Organizations can assess their risks using qualitative and quantitative studies, statistical approaches, and expert opinions. This holistic view helps organizations prioritize high-risk threats and make better AI adoption decisions, balancing innovation and risk mitigation.
Regulation compliance The GDPR, CCPA, and EU AI Act protect sensitive data worldwide.
Those who break these laws face severe fines and punishments. AI model risk management can help organizations comply and stay in good standing as AI rules grow almost as quickly as the technology.
Resilience operations Artificial intelligence risk management helps companies minimize disruption and maintain business continuity by addressing AI system issues in real time. By helping organizations build defined AI management practices and processes, AI Model Risk Management may improve accountability and sustainability.
Trust and openness increased AI Model Risk Management prioritizes trust and transparency to promote AI ethics.
Executives, AI developers, data scientists, users, policymakers, and ethicists are involved in most AI model risk management processes. AI systems are designed and deployed ethically with all stakeholders in mind with this inclusive approach.
Constant testing, verification, and monitoring Testing and monitoring an AI systemās effectiveness helps organizations spot new dangers faster. Monitoring helps organizations comply with regulations and mitigate AI hazards earlier, lowering threats.
AI Risk Management AI technologies can improve labour efficiency, but they can pose risks. Nearly every enterprise IT can be misused.
Organizations can use generative AI. It should be treated like any other technical tool. That involves knowing the risks and taking precautions to prevent an assault.
IBM watsonx.governance lets organizations administer, manage, and track AI initiatives in one place. Watsonx.governance can control any vendorās generative AI models, assess model health and accuracy, and automate compliance operations.
Read more on Govindhtech.com
#AImodel#AI#riskmanagement#ibm#ibmwatson#cybersecurity#generativeai#technology#technews#news#govindhtech
0 notes
Link
#AI#AICollaborationandTools#AIEducationandOutreach#AIEthicsandSafety#AIResearch#AIResearchandAdvancements#aiwebsite#AIWeekly#arXiv#best10AI#chatgpt#ComputerVision#DataAnalyticsandInsights#DeepLearning#DeepMind#FinancialServices#HealthcareandLifeSciences#IBMWatson#ImageandVideoAnalytics#InternetofThings#Kaggle#LanguageModels#MicrosoftAI#MITTechnologyReview#NaturalLanguageProcessing#NVIDIAAI#OpenAI#ProductionDeployment#ReinforcementLearning#ResearchandExperimentation
0 notes
Text
Top 10 IBM Development Service Providers Around the World
Ināour tech-driven world, business cannot afford to get left behind. Whether cloud or AI or enterprise systems ā IBM has powerful solutions thatācan help companies scale smartly. But hereās the hitch: receiving the full benefits of IBM technologies reliesāon selecting the proper developer associate.
Here are the Top 10 Global IBM Development ServiceāProviders As of October 2023 They are known for providing tailored solutions using the IBM ecosystem ā including IBM Cloud, Watson, AI, data analytics and enterpriseāautomation.
What Makes These Companies Stand Out?
Excellent domaināknowledge of IBM technology and toolsets
Tailored solutions thatāare scalable and secure
Industry experienceāin diverse industries including finance, healthcare, retail, and more
Comprehensive supportāā from consultation through implementation and beyond
With up to October 2023 data, Do you imagine Picking the right IBM development company not only get you fasterāinnovation or smoother processes but also a competitive advantage in IBM.
These companies donāt just build tech ā they serveāas strategic growth partners. Their goal? To leverage the power ofāIBMās ecosystem for your benefit.
Final Thoughts
IBM technologies are powerful, but they need the right hands to make an impact. The companies on this list have a proven track record of delivering exactly that. If you're planning to take your business to the next level with IBM solutions, partnering with one of these top service providers is a smart move.
For more detail: https://www.linkedin.com/pulse/top-10-ibm-development-services-companies-world-izhar-ali-qiwpf/
IBMConsulting #IBMWatson #SoftwareDevelopment #DigitalTransformation
SEO #ContentWriting #TechTrends #CloudComputing #SEOStrategy
Top10Companies #Innovation #BusinessGrowth #TrustedPartners #GlobalTechLeaders
1 note
Ā·
View note
Text
Top Innovations of 2025!
#ArtificialIntelligence #PowerfulAI #TechInnovations #GPT4 #AlphaFold #IBMWatson #TeslaAutopilot #ARDINATE #FutureTech #AIRevolution
0 notes
Text
youtube
Inside IBMās AI Revolution: From the 1950s to Now, Explore IBMās groundbreaking AI evolutionāfrom the earliest 1950s experiments to the transformative power of Watsonāand see how theyāre shaping the future of technology. https://www.youtube.com/channel/UC3o4B5eoAcewBjxvaeC5Rxg?sub_confirmation=1 IBMās role in the development of artificial intelligence traces back to the 1950s, when the company began experimenting with early AI programs that would lay the groundwork for modern computing. Known for its long history of technological milestones, IBM accelerated its AI research for decades, culminating in the unveiling of IBM Watson in 2011. This landmark system demonstrated the potential of AI to interpret vast amounts of data and generate insights in real-timeāan achievement that caught the worldās attention. Watsonās victory on the game show Jeopardy! was more than just a pop-culture moment; it showcased the real capability of AI to process natural language, learn from large data sets, and respond with accuracy previously thought impossible. Since then, IBM has continued to expand the boundaries of AI, focusing on solutions that serve a broad range of industriesāfrom personalized medicine in healthcare to risk analysis in finance. By partnering with multiple organizations, IBM aims to democratize AI applications, making them both accessible and impactful on a global scale. Throughout this journey, IBMās commitment to research and innovation has never wavered. Its strategy involves deep collaboration between scientists, developers, and industry experts who together push the limits of AI technology. As AI continues to evolve, IBMās focus on ethical and responsible implementation remains a guiding principle, underlining the companyās vision to leverage AI for solving some of humanityās most complex problems. Today, IBMās legacy is a testament not only to how far AI has come, but also to the transformative potential it holds for the future. š For The Latest Stories on luxury travel, getaways goods, the rich, companies, Top 10ās, biographies, Lavish History, news, and more ��� https://www.youtube.com/@Lavishangle š For business enquires contact us at full4sog (@) gmail dot com š¬ Don't forget to leave your thoughts in the comments below. We love hearing from you! š and hit that bell to stay updated on all new videos we release. #lavishgetaways #thelavishandaffluentangle #thelavish&affluentangle #tlaa #shorts #shorts #shortvideo #shortsvideo #shortsviral #shortvideos #shortsyoutube #shortsbeta #viralshorts #viralshort #viral #viralreels #youtubeshorts #viralyoutubeshorts #viralshorts #viralshort #viralshorts2024 #IBMHistory #ArtificialIntelligence #AIRevolution #IBMWatson #MachineLearning #TechInnovation #DataAnalytics #HealthcareAI #FinanceSolutions #ResearchAndDevelopment #TechnologyLeaders #HistoricMilestones #FutureOfComputing #EarlyAI #GlobalInnovation #ScienceAndTech #ResponsibilityInAI #IBMResearch #TechPioneers #ComputingLegacy #AIJourney #NextGenTech #IndustryCollaboration #TransformativePower #50sAIOrigins via The Lavish & Affluent Angle https://www.youtube.com/channel/UC3o4B5eoAcewBjxvaeC5Rxg January 06, 2025 at 06:30PM
#lavishgetaways#luxurylifestyle#luxuryhotels#luxurytravel#luxuryliving#traveltheworld#travelgoals#Youtube
0 notes
Text
did you finance your labs on heres how to salvage allyour progams .@googleai .@goo gle .@ibmwatson .@ibm .@apple .@tim_cook @darpa @dwave sergey rainbowtimmy yellow the remaybe a way to salvage all your #qubit operations. contact wife and @atom @us_stra tcom .@us_stratcom about newatom tweak 4.4BN EUR + tax +5%persold/made/monetised unit
did you finance your labs on heres how to salvage allyour progams .@googleai .@google .@ibmwatson .@ibm .@apple .@tim_cook @darpa @dwave sergey rainbowtimmy yellow theremaybe a way to salvage all your #qubit operations. contact wife and @atom @us_stratcom .@us_stratcom about newatom tweak 4.4BN EUR + tax +5%persold/made/monetised unit I am Christian KISS BabyAWACS ā Raw Independentā¦
View On WordPress
0 notes
Text
Know the Details About the IBM Watson Architecture

The IBM Watson architecture is a Linux program based on the IBM power system. Since Watson is a user-friendly, human-generated tool to compute in natural language, the architecture is simple and unique as well.
Details about the IBM Watson architecture
IBM Watson architecture is a parallel tool based on the IBM Power 750 that racks the standard configuration. It runs on the Linux based on the Novellās Linux Enterprise Server and IBM power combined. It is made up of 16 Terabytes of memory along with 4 terabytes of the clustered storage system.
The Watson is racked with 10 random racks that include the server, networking system, data analytics, and shared disk system, and cluster storage, controller. The IBM Watson has a total power of 2880 power banks.
The deep insights of the Watson
The IBM Watson architecture is divided into different racks and steps. Here is the basic architecture of the Watson:
Service management
To deliver, plan, and operate the cloud services that are offered to the customers. Alphonic and other web development services use this facility to manage the clouds of the customers.
Cognitive
The IBM Watson architecture takes care of computing and unlocking the unstructured and structured data with intelligence and understanding by developing the insights of the data.
Private cloud
It manages all the private cloud of the individual organization.
Microservices
The cloud-native approach towards building the mobile and web application with microservices architecture.
Data and analytics
This IBM Watson architecture helps in developing solutions and analyze the data including web and social platform. It reports on the data and understands the diverse structure to drive insights and visualization.
Internet of things
It connects the various IoT devices and builds apps to gain insight of the IoT data and the clouds with cognitive services.
Virtualization
Virtually creates and uses the software using the IBM cloud software and the platform to store the data for the client.
API transformation
Exposes the business assets according to the new digital ecosystem through the API.
Blockchain
It is an integral part of the IBM Watson architecture since it records the history of the transaction and accounts them according to different categories.
E-commerce
The Watson architecture deploys, develops and e-commerce solutions. The allophonic web development services use this feature to develop an e-commerce platform to securely connect the client data to the cloud.
Web application
Like Alphonic web application uses this architecture to develop integrated runtime services and deploy the apps.
For more information visit www.alphonic.in or call at +91 9887133338 , drop an email at [email protected]
#ibmwatson#ibm#technology#b#ai#iot#sanfrancisco#salesforce#alphonicnetworksolutions#web application#blockchain#ibmwatsonarchitecture#web development#mobile application development#onlinebusiness#alphonic#customer service#startup#startuplife
1 note
Ā·
View note
Link
For hundreds of years, the strategies of answering complicated riddles and complex puzzles have been painstakingly devised by Zen Buddhists in ākoan-riddlesā, used for unravelling truths!
1 note
Ā·
View note
Photo

Scenes from the digital transformation conference with @ibm , @cyberspacenaija delegates from different parastatals in the federal capital. #ibmcloud #ibmwatson #ibmcloudpakfordata #digitaltransformation #machinelearningengineer #machinelearning #datageek #pythonprogrammer #analytics #instaleadership #instamemories #instatech #instatechnology https://www.instagram.com/p/CmCyOC1tosb/?igshid=NGJjMDIxMWI=
#ibmcloud#ibmwatson#ibmcloudpakfordata#digitaltransformation#machinelearningengineer#machinelearning#datageek#pythonprogrammer#analytics#instaleadership#instamemories#instatech#instatechnology
0 notes
Text
The Future of Work: How BPR Can Help Your Business Change

BPR News
By redesigning key business processes, business process reengineering (BPR) greatly enhances performance, speed, and effectiveness. Continuous innovation and change that improves processes from start to finish and gets rid of waste are examples ofĀ BPR. By removing steps and streamlining processes,Ā BPRĀ makes the best use of resources.
BPRĀ challenges organizational conventions and procedures by redesigning business processes. It usually seeks radical process transformation. It should not be confused with business process management (BPM), a more incremental approach to optimizing processes, or business process improvement (BPI), which comprises any systematic effort to enhance processes. This blog showsĀ BPRĀ cases that benefit from BPM.
What is business process reengineering
BPRĀ was a management method developed in the early 1990s to alter business operations by drastically restructuring processes. The 1990 Harvard Business Review article āReengineering Work: Donāt Automate, Obliterate,ā by Michael Hammer, and the 1993 book Reengineering the Corporation by Hammer and James Champy popularised the concept. Ford Motor Company, a 1990sĀ BPRĀ pioneer, streamlined its manufacturing processes and increased competitiveness.
Organizations of all sizes and industries reengineer business processes. First, determineĀ BPRĀ goals, then review the existing condition, identify gaps and opportunities, and process map.
Business Process Reengineering BPR definition
Effective leadership, change management, and continuous improvement are needed to implement business process reengineering. To enable new procedures and significant change, leaders, senior management, team members, and stakeholders must advocate theĀ BPRĀ project and give resources, support, and direction.
Examples of BPR in Companies
Streamlining supply chain management
BPRĀ for supply chain optimisation requires rigorous review and redesign of logistics, inventory management, and procurement. Rethinking procurement, introducing just-in-time inventory, optimising production schedules, and restructuring transportation and distribution networks are all possible supply chain overhauls. To automate and improve things, you can use SCM, ERP, and advanced analytics tools. Predictive analytics helps figure out what people will want and how much to stock, and blockchain technology can make the supply chain more open and easy to track.
Benefits:
Improved efficiency
Reduced cost
Increased transparency
BPRĀ is a crucial technique for organisations seeking to revamp their CRM operations. Business process reengineering for CRM involves integrating customer data from several sources, employing advanced analytics for insights, and optimising service workflows for personalised experiences and lower wait times.
CRM BPR uses may include:
Integrating CRM software to centralise customer data and enable real-time analytics Using multichannel communication to give consistent experiences across touchpoints Equipping frontline staff with training and resources to provide excellent service BPRĀ helps firms anticipate consumer demands, personalise interactions, and resolve issues quickly.
Benefits:
360-degree consumer view
Enhanced sales and retention
Faster problem resolution
Digitising administrative processes
To eliminate human errors, organisations are digitising and automating administrative operations withĀ BPR. This transition involves replacing manual, paper-based procedures with digital systems that use RPA for everyday tasks.
This could involve automated invoicing, payroll, or HR. This can boost efficiency, accuracy, and scalability, helping the company run better.
Benefits:
Shorter processing
Fewer mistakes
Increased adaptability
Process improvement for product development
BPRĀ optimizes product development from inspiration to launch. This revamp evaluates and redesigns workflows, promotes cross-functional collaboration, and innovates employing new technology. This can include cross-functional teams to promote communication and knowledge sharing, agile methodologies to promote iterative development and rapid prototyping, and PLM software to streamline documentation and version control. TheseĀ BPRĀ activities help companies cut product development cycle times, respond faster to market demands, and offer creative, customer-focused goods.
Benefits:
Faster market entry
Increased innovation
Superior product quality
Technology infrastructure update In this age of rapid technological innovation,Ā BPRĀ is essential for companies updating their technology infrastructure. Migration to cloud-based solutions, adoption of AI and ML, and integration of heterogeneous systems improves data management and analysis, enabling more informed decision-making. New technologies boost performance,Ā cybersecurity, and scalability, setting companies for long-term success.
Benefits:
Improved performance
Enhanced security
Increased innovation
Reduce staff redundancies
Many companies useĀ BPRĀ to reorganize and decrease redundancies in response to changing market dynamics and organizational needs. Strategic initiatives can streamline hierarchies, consolidate divisions, and outsource non-core services. By optimising labour allocation and eliminating duplicate tasks, organisations can cut costs, boost efficiency, and prioritise resources.
Benefits:
Cost-saving
Increased efficiency
Core competencies first
Operational cost reduction
Businesses may efficiently detect inefficiencies, redundancies, and waste viaĀ BPR. Organizations can streamline and cut costs.
BPRĀ redesigns processes to optimize resource allocation, decrease non-value-added work, and increase efficiency. Automating tedious operations, reorganising workflows to decrease bottlenecks, renegotiating supplier contracts for better terms, or using technology to improve collaboration and communication may be needed.
Benefits:
Improved efficiency
Lower costs
Increased competitiveness
Improving output quality
From production to service delivery,Ā BPRĀ improves output quality.Ā BPRĀ usually improves KPIs.
Implementing quality control, promoting continuous improvement, and leveraging customer feedback and other metrics to drive innovation can increase output quality.
Technology can automate operations. Eliminating distractions helps staff focus on providing high-quality products and services. It promotes client trust and loyalty and helps the company succeed.
Benefits:
Better client satisfaction
Fewer mistakes
Better brand image
Optimizing HR processes
BPRĀ is essential for HR optimisation. Automating onboarding with simple portals, optimising workflows, establishing self-service portals and applications, employing AI for talent acquisition, and data-driven performance management are examples.
Engaging employees helps attract, develop, and retain outstanding talent. HR methods aligned with company values can boost employee productivity, satisfaction, and business performance.
Benefits:
Recruiting faster
Enhanced employee engagement
Allocating talent strategically
Examples of BPR:Ā Case studies
BPRĀ methodology and use cases work together to assist clients in the following case studies.
Bouygues leads French telecom AI
Bouygues Telecom, a major French communications provider, suffered with ageing systems that couldnāt handle many support calls. The result? Bouygues risked being displaced by competition as frustrated consumers waited in call lines. Bouyguesā pre-IBM Watsonx AIĀ implementation with IBM was a blessing. Phase 1 preparations were ideal for phase 2ās AI integration into the telecom call centre.
Bouygues receives over 800,000 calls a month usingĀ IBM watsonx Assistant, and IBM watsonx Orchestrate frees agents from monotonous duties to focus on higher-value jobs. Pre- and post-call workloads dropped 30% for agents. In addition, 8 million customer-agent exchanges, previously partially analysed, have been accurately summarised for actionable insights.
These innovations have made Bouygues a customer care disruptor, reducing operational expenses by USD 5 million and putting them at the forefront of AI technology.
Customer-centric transformation at Finance of America fosters lifetime loyalty. Finance of America co-created with IBM to tailor their operations to consumers, delivering value for them and prospective homebuyers.
FOA expects to increase their customer base in three years after this transition. Revenue and income should rise by 50% and 80%, respectively, in the same timeframe. Finance of America is ready to offer debt advisory and other services that will build customer loyalty.
Business Process Reengineering
IBM businessĀ process reengineering analyses fundamental processes to identify and redesign areas for improvement. By stepping back, strategists can examine supply chain, customer experience, and finance.Ā BPRĀ services specialists can integrate new technologies and rework procedures to improve the business. They can help you create intelligent workflows that boost profits, eliminate redundancies, and cut costs.
Read more on govindhtech.com
#ai#IBM#IBMWatson#reengeneering#bprservices#news#technews#technology#technologynews#technologytrends#govindhtech
0 notes
Photo

We offer šš ššØš§š¬š®š„šš¢š§š , šš§šš®š¬šš«š¢šš„ šš«šš¢š§š¢š§š š©š«šØš š«šš¦š¬ in various šššš”š§šØš„šØš š¢šš¬, ššš šššÆšš„šØš©š¦šš§š, and šš„šØš®š š¬šš«šÆš¢ššš¬ as well. šš®š« ššØš®š«š¬šš¬ šš«šØšÆš¢šš: * Artificial Intelligence * Machine Learning * Cloud Computing * Java Programming * Ethical Hacking * IOT, IOS #IBMWatson #IndustrialTraining #datascience #artificialintelligence #machinelearning #courses #onlinetraining (at Mohali, Chandigarh) https://www.instagram.com/p/CSG3izarMkk/?utm_medium=tumblr
#ibmwatson#industrialtraining#datascience#artificialintelligence#machinelearning#courses#onlinetraining
0 notes
Link
#ibmwatson#ibmwatsontraining#learnibmwatson#ibmwatsoncourse#ibmwatsoncertification#ibmwatsonanalyticscertification#ibmwatsontrainingcourses#ibmwatsoncertificationcost#cloud#certification#ibm#ibmtraining#globalknowledgetechnologies#training#gkt#ittraining#itcourses
0 notes
Photo

Muita coisa boa por vir. Sexta depois de feriado Ć© dia de correr mesmo, de trocar conhecimento e de fazer acontecer. šŖš» #ibm #bigdata #bigdataanalytics #datascience #ibmwatson #watson #artificialintelligence #marketingpoliticodigital #marketingdigital #marketing #rodrigogadelha #marketing #marketingpolitico #marketingeleitoral (em IBM Brasil)
#marketingeleitoral#bigdataanalytics#marketingdigital#marketing#watson#bigdata#marketingpolitico#marketingpoliticodigital#datascience#ibm#rodrigogadelha#ibmwatson#artificialintelligence
2 notes
Ā·
View notes
Photo

Watson Portrait #ibmwatson #portrait #ai #watson #analysis #ogilvy #ibm
2 notes
Ā·
View notes