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Agile Mumbai 2022 - Dr. Suresh A Shan | Keynote - Impact of Artificial Intelligence in Software Development Life Cycle

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Agile Mumbai 2022 - Dr. Suresh A Shan | Keynote - Impact of Artificial Intelligence in Software Development Life Cycle

  1. 1. AGILE MUMBAI 2022 CONFERENCE Impact of AI Artificial Intelligence in software development life cycle. If you state first ,IT is Technical Feature... if the end user finds & state’s first, then it is Bug.., A little bit about the Speaker Dr. Suresh A Shan AI thought Leader, III decades of MMFSS BFSI NBFC Tech Expert, D.C Consultant, CDBDO – TRCIMPEX Hon. Chairman Mumbai chapter- Computer Society of India -CSI
  2. 2. What is the AI Software Development life cycle? •Requirements analysis; •Design; Development; •Testing; Deployment; Save Time, Money Efforts, Creator(developer), Corporate, Customer Corporate Digital  Customer Digital
  3. 3. • Impact of AI Artificial Intelligence in software development life cycle. • How artificial intelligence can improve software development process? • #1 – Saves time with automated code generation • #2 – Runs quick and efficient tests • #3 – Generates unique software designs • #4 – Enables rapid prototyping • #5 – Automated project budgeting • AI platforms • Chat-bots • Deep learning software • Machine learning software Developer working for startups or big organization as a fresher makes huge difference using adopting applying technology. • With an increasing demand for scalable, secure, and unique applications, there is tremendous pressure on the developing community. Impact of AI Artificial Intelligence in software Design development life cycle.
  4. 4. How to design an Artificial Intelligent System. • Concept development, solution discovery, and resource estimation to get ready for your first AI system production • AI capabilities like ML, NLP, expert systems, automation, vision, and speech; A robust cloud infrastructure. • Customer empathy; Experiments; • The AI solution should be consisting of smaller components; • Avoiding bias arising from wrong data. • During this phase, you need to evaluate the various AI development platforms, e.g.: Microsoft Azure AI Platform; Google Cloud AI Platform; • IBM Watson AI platform; Big-ML; Infosys Nia. • AI Bihar insurance claims ICU is missing • AI Based gold loans and model patterns 360 view •Microsoft Azure AI Platform; Google Cloud AI Platform; •IBM Watson Developer platform; BigML; Infosys Nia resources.
  5. 5. Data Capture, The Model Has Changed. Diwali apps sale using AI • The Model of Generating/Consuming Data Policy Process procedures has Changed Old Model: Few companies are generating data, all others are consuming data New Model: all of us are generating data, and all of us are consuming data
  6. 6. Technologies & software professional's differ & standardization across In India E– Business is less to do with Electronics & more & more to do with Emotions. google maps & Google earth
  7. 7. Technologies in policies for software development from regulators –RBIH –REBIT Awareness on IT Policies and process procedures social web sites, mobility IOT security guidelines
  8. 8. Why AI Technology for software development life cycle 8 From SAS Desk ; All Rights SAS @ Reserved
  9. 9. From SAS Desk ; All Rights SAS @ Reserved AI Technologies in process policies procedure implementation.
  10. 10. Technologies in policies implementation. What is the difference between banking and NBFC financial services transactions systems.
  11. 11. Main Heading 11 From SAS Desk ; All Rights SAS @ Reserved AI Technology – – Business benefits to Banks Financial services AI based customer stories “ earn to pay model” cloud computing.
  12. 12. Generic Frame work Host Applications Processing Layer ODS & DW
  13. 13. OLA / Uber Google data
  14. 14. From SAS Desk ; All Rights SAS @ Reserved 14 Using Right Technology at Right Time Rural India Financial Services Impact of AI Artificial Intelligence in software development life cycle in BFSI Sector
  15. 15. 15 From SAS Desk ; All Rights SAS @ Reserved DNA :- Create new Markets through Service culture Innovation. DNA: BASICS – 3 B Innovation: External & Internal users need solutions that are new and different adopt to Rural in nature as BELIEF Action: Take 100% responsibility with Integrity on our commitments and execute them flawlessly as local our - BEHAVOIR Triumph – Sense of Accomplishment from every action, every time resulting in Joy and Happiness for all like our traditional festivals - BUSINESS Pushing the boundaries of Minds and Machines If you want to break through look outside your current environment. A key quality to have is to be authentic About MMFSS – BITS - Innovations Business Process Automation for Office Operations
  16. 16. Synopsis – What is the Belief base ? Why? 16 From SAS Desk ; All Rights SAS @ Reserved
  17. 17. Thesis – Belief Why ? How ? What? 17 From SAS Desk ; All Rights SAS @ Reserved
  18. 18. Thesis on Technology Behavior Assumptions between Urban Rural Globally 18 From SAS Desk ; All Rights SAS @ Reserved
  19. 19. Synopsis – Belief  Behavior  Business 19 From SAS Desk ; All Rights SAS @ Reserved
  20. 20. From SAS Desk ; All Rights SAS @ Reserved 21 Impact of AI Artificial Intelligence in software development life cycle. Project powered by Geospatial
  21. 21. From SAS Desk ; All Rights SAS @ Reserved 22
  22. 22. From SAS Desk ; All Rights SAS @ Reserved 23 Info on Map Pins - just by a click
  23. 23. From SAS Desk ; All Rights SAS @ Reserved 24  Map Pins were used to express instant information's in abundant to the basic user,  The maps pins had been designed uniquely for Mahindra Finance’s day to day activities, These Pins are a state of art design, which gives the details of the customer category by just visualizing the designed map Pin’s (details like Loan type , Loan product etc).  Further for attaining all complete details all the branch team has to do was just to click on the MAP PIN to view it (details like customer name, address, Location, Mobile no# etc).  These map pins equips the branch team with all the required intelligence on the customer profile like .. (a) Loan Type => New , Refinance, Both, (b) Customer Type => No OD cases (Green pins ), OD Cases (Yellow pins ), NPA cases (Red pins ) (c) Product Type => 2W – 2wheller, AS –Auto Sector etc, (d) Count available => Customer counts were available below the map pin Following figures will display the complete details of the same… Innovative Map Pins
  24. 24. From SAS Desk ; All Rights SAS @ Reserved 25 Complete Product wise information..
  25. 25. From SAS Desk ; All Rights SAS @ Reserved 26 Navigation(Route ) was never been such easier before
  26. 26. From SAS Desk ; All Rights SAS @ Reserved 27 Pictographically – AI/BI Dash Board
  27. 27. AI Thoughts- case studies 001 7-B’s-Belief Behaviour Business Best Benefits BeE Bharat. • AI Future online offline transactions. Differentiate • AI Digital transformation. – corporate to customer • AI HHD data capture climate update - • AI base audit tracking • AI base NOC issuance ( last mile ) • AI Vehicles financed in Jaipur seized in north east • AI drivers life style when use vehicles and cost of living • Mathematics – friends should be like no. 9 and not like no.8 – Gandhiji • Astrology message for receipts • Info-comm visual graphics – GJ SOMNATH temple / Sai baba shirdi – house • RAMA KRISHNA bank & NBFC at rural India • KARNA ARJUN entrepreneur blessed • For women stand or sit & cook – avoid sisrein • Cyber security compare with rural ritual goddess •
  28. 28. Technology is neither good nor bad nor it is neutral. It certainly cant be un-invented – The future & of
  29. 29. G 1-06-09 14:55 PROCESS COMPLETED
  30. 30. Rice & Wheat – south & north Indian respect Indians for excellence from AI
  31. 31. Indian AI Artificial Intelligence understanding.. 1. Explosion in unstructured text worldwide:- 2. Ambiguity & Variability of Language Anything we say in the natural language can mean multiple things depending on the context in which it is said, who is saying in it, who is listening it, how it’s being said, and so on. That’s ambiguity.. The variability which we can express the same thing in different ways, and these two together collude to make this process understanding natural language so difficult. 3. Machine Learning to understand and interpret Unstructured data:- these are the areas in India we are still struggling with. The conventional way fails or falls, then we develop machine learning methods that are sensitive to context- e learning – less of electronics more of emotions.. Challenges to scale AI solutions. Adoption to structured from un – structured we have not done much yet.. Annotation today most of the information on supervision, all annotations are not feasible and unrealistic..
  32. 32. Indian AI Artificial Intelligence understanding.. Building Relevant & Required AI that lives and grows with available and affordable resource in the real world and solve the problems what we have…, development on analytics tools Build AI can scales and adopt… different environments beyond the vertical- (climate update ex) Key challenges and building a sustainable model:- - Finding and retaining the talent.. Less relevant resources available. Sustainable revenue Methods and models. – applications of machine learning in E comerce ( auto door close speed printout – no customer requirement- astrology messages:- IT should discover hidden patterns of data, and leverage the patterns to predict future data. How AI and IOT, mobility ,Cloud, big data, blend in industrial financial channels build by innovation opportunities. The chatbot revolutions:- rise of the conversional user interface. -Its an interface that enables users to complete a task through conversational interaction with a machine or human. -Conversational User Interface:- Ease of doing everything on ONE screen with the GUI and multi- lingual . It is mostly used interface on the smart phone . Technology that feels or connect like a friend.. - MMFSL use case on rural using multi-lingual chatbots - the google play ML cloud ; the IBMs Watson;
  33. 33. Traditional business intelligence :- :: analytics is optimizing decision making in situations of uncertainty :: analytics is finding the optimal path to a desired future :: analytics is not more data! :: optimization means finding the best path among multiple options :: and inferential statistics to forecast risk :: stage two uses techniques such as predictive modeling 2. :: analytics stage two predicts the future 3. Analytics is data about the desired future
  34. 34. Largely felt where we can really leverage Artificial Intelligence. •It improves overall team Performance , productive effective and efficient, •Capture and enhance timely Customer communications with Build transparent Trust. •Understand the happenings through all entities and capture all the Emotions. •Ensure the customer Experience is a consistent on going activity. •Use multi-lingual and Need connect to personalize the content and delivery. •Market team should work upon the clarity on the branding vision, mission through campaigns. •Convert as much as un-structured to structure using the components of people & Process. •Get the Data captured in the systems are analyzed and used at his best. •Bring the business customer predictions to engage the customer for his life long using content. •Build the organizations with one voice one message with data based decision making. •Do execute as much as research on the data to enhance the organization to the next level. •Bring a professional transparent timely supported system to connect the customer 24/7.
  35. 35. Data Management for Analytics: Five Best Practices 1.Simplify Access to Traditional and Emerging Data. 2.Strengthen the Data Scientist’s tools With Advanced Analytic Techniques. 3.Scrub Data to Build Quality Into Existing Processes 4. Shape Data Using Flexible Manipulation Techniques. 5.Share Metadata Across Data Management and Analytics Domains. 6.Trusted Data, Proven Analytics
  36. 36. Data Management for Analytics: Five Best Practices 1.Simplify Access to Traditional and Emerging Data. 2.Strengthen the Data Scientist’s tools With Advanced Analytic Techniques. 3.Scrub Data to Build Quality Into Existing Processes 4. Shape Data Using Flexible Manipulation Techniques. 5.Share Metadata Across Data Management and Analytics Domains. 6.Trusted Data, Proven Analytics

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