Hi everyone. Thank you for joining me today. My name is Kumar. I'm executer to present on the topic that is critical for modern enterprise a power testing preventing SAP and Salesforce instant before they happen in the production environment. Enterprise instance often arise from. Testing of Complex SAP and Salesforce system. But here the good news is this one. By leveraging artificial intelligence and mission learning organizations can identify the potential failures before the impact to the productions systems. This approach dramatically reduced the testing cost while boosting the test coverage and the accuracy with the, along with the quality validations, or the next 20, 30 minutes, we are going to explore why this matters, the challenges we face, how AI can transform the test strategies. Let's dive in. Let's start with the big picture. SAPS four. HANA and Salesforce are the backbone of the countless global business operations, covering everything from simple chain management, which is supply chain man management tool, customer relationship management. Now with artificial intelligence, open this platforms. Think. Predict you, analyticals and SAP, sales and distributions, metal management, finance. Transport management modules, so many modules and our automated code to cash business process in Salesforce system, we are witnessing a revolution. But this revolution comes with challenges. AI introduce dynamic adaptive systems that traditionally testing methods struggling to keep on. Considering the AI system in SAP automates the general entry to ensure with the SO and JXP regulations. When we are talking about the SO and JX P regulations, it's a very important for the complaints and regulator industry. A single mismatch could lead a significant financial losses or legal issues or a picture in Salesforce platform. Mispricing a contract, it going to be break the customer trust as well as it going to impact the revenues. These are not just possibilities. These are the real risks in the current environment. Today, the tradition testing, it's a static scripts with manual process for the shot and automation. Also, whatever the automation which we are doing that is also have the some limitations with the test data as well as this one, the actions which we perform through automations, but because of a algorithm. Over the time, this demands new approach with the continuous quality validations. Along with this one, complaints, regulations, controls to maintain the data integrity and system performance, user confidence and coming. What does this means? Intelligent testing to bring in the table. It's a game changer. This approach leverages the advanced tool like Tic Toska Robotic Process Automation. We also call as A RPA. Frameworks and a agent simulate the real world scenario, actively identify the issues in SAP, for instance, intelligent testing can monitor aid driven outputs across all the tracks like. S-E-M-G-T-S-S-D-M-M, uh, it's gone. It's, it's, it's, uh, all the tracks including Salesforce, but catching the issues before the escalate in the Salesforce, validating the price, accuracy and workflow efficiency, the key factor in the customer satisfaction and business success. And does, and it does not stop here with the raise of generative, a stakes even get even higher. The technology capable of creating the content of making decision requires testing the beyond the functionality. Now, intelligent testing, not only verifying through validation, but also verify the ethical Aus aligning with the certifications like. SAPA ethics and mitigating the biases that could, that could be otherwise. It's a compromise of fairness. Without this, the, we risk the deployment failure complaints, breaches, and loss of credibility. That could be industry back. Let me share some real time example, my own experience, um, where I serve this as, as a senior quality assurance guy, we design and developed few agents that generate the test score, execute the test plans in the tool, the agent will analyze this one, the changes the live compare reports, what SAP transports. Which is changes. And if coming back to the sales force, what GitHub, GitHub pull request is it? How this automatically determines the regression impacts? This is one project where the, our intelligent tool reduce the manual testing by 40 to 50 percentage. By reading this one, the SAP changes, what are the changes they brought? What are the business process they're changing? What are those object and what are the Salesforce objects has been changing by going through this one full request. Our change summary. By reading this one, our intelligent tool, well intelligent testing related automation, ai. It ensures this one. We have comprehensive coverage for the AI and enhanced future. And, uh, without overlooking the critical, the complaints is validation. If I going through further here on the intelligent testing, the, um, the RPA bots, generally RPA bots we create for automating the repetitive rule-based tasks. The bots is mainly familiar with this one. Financial workflows such as general inter posting, accounting, reconciliations, various analysis and financial statements. Scratch check with the high precisions. But not only that, even, uh, the eds, electronic data interchange, electronic data interchange. Sales order tions the implementing of RPA bots in controlling testing, we are minimizing the human error and boosting the efficiency here. And the recent audits also where the bots identified the discrepancies in SOX complaint as process and the manual review. It might be missed on this, the SOX complaints is that manual reviews might be missed. The permitting, the potential regulation is, let's not to forget this tic, where the corner stones of the intelligent testing I have used to build, uh, automated regression test cases for multi modules in SAP as well as Salesforce, for instance, upgrades and the roll loads where SD and mm, which is sales and distribution. Mm. Integrations comes. With the aid driven automation, the Toska module based allow us to simulate the end number of scenarios defect early escalating, the go live by the weeks. It's a, in the, especially in the logistics area, the real world application, it's not only ensure the seamless integrations and transitions from the legacy system to SAP, but also make them the SOCs and JXP stakeholders Confluence. Let's, we'll go with the, some of the examples here, how the intelligent testing safeguard the, our operations dive into this one, into AI much faster and more reliable in the deployment. But here the question for all of us, are we prepared for this shift? A become more embedded in our workflows. The demand of intelligent testing will only grow the organization that adapts will lead the package while this don't, may fall behind. This is not just about technology, this is about the building trust. Ensuring the complaints is unlocking the full potential of AI enterprise solution. So what can we do first? Investing in the right tools, platforms like Tosca, RPA, frameworks at this time. Second, trying the teams beyond the tradition testing. Embarrassing the AI adaptability. Third culture, which is continuous improvement, where testing is, involves the technology. Now, the intelligent testing, it's just a technical requirement. It's a strategic as well. Let's we go to the deserve to shape the future where the technologies empower us all. So what will happen if this intelligent, it'll not serve this one if you're not covering the major testing. What will happen on this? The main thing, this one, it, the testing. If it's not happened, the impact will be on the business operations. It'll damage this when your relationship with the customer, it, uh, stick the loss of the financial, it'll impact the business. So that is the reason it's a very critical to have this intelligent testing. So far, we've gone through this one, why the intelligent testing is required and how that makes the difference. On top of this one, manual testing and automation testing, understanding the enterprise testing challenges. Now, let's break down the challenges First. The complexity landscapes, like SAP environment, the, the, the SAP is having a multiple SAP system and also S four SAP, S four hana. It has a multiple modules that interact interdependencies and also it's creating a cascading failure of potential as well. In the Salesforce. On another hand, bringing the extension, customization, frequent updates, the system rarely operates isolation. But this integrate with the legacy applications, external applica APIs application program links and the data warehouses. The tradition testing has a limits where the manual testing creation is a time intensity. After it misses the full coverage of the testing of the system interaction. On top of this one, resource constraints force the trade off between the thorough testing as well as delivery timelines. On top of this one, the statics test suites become outdated as system evolves leading graduation degradation of efficiency. So they understand here, there's this, each client, each business operation. The landscape as well as testing challenge just will be different. I'll try to cover a few of them here. So, as I mentioned earlier, SAPS, Fourna and Salesforce are powerful platforms. That drive the critical business operations and a right now, we are using to enhance our testing efficiently become the forbit challenges. Let's, I'll drill on on this. First, let's consider the scalability integrations, which we have discussed on this one, which is interacting with the multiple models, which is thousands of transaction data flows across the global terms. Each transactions, which we are, we are rmi, that will create a number of, uh, table data along with this one that will impact with the SOX and JX P process controls as well. Salesforce with the CPQ with a multi-cloud setup, add the layers of customization where the price rules has been changed. Now understanding this by going through this one S four as as well as Salesforce, they have challenges in the data dependencies. Both platform rely on the vast data sets, like example, SAP, for financial recognition, Salesforce for customer analyticals, ensuring this one data errors production without compromising the security constraint hurdle. In my experience, I've since many project where inconsistent test data lead to missed defects in the integrations, which can be any SDMM, anything, also discovering the post go live, it'll be costing to the weeks of rework and the project delays. The third one, this one, the face of change adds the pressure with the continuous upgrades. SAP has the releases, which is quarterly or service back upgrades, and Salesforce has this one seasonal upgrades, which is winter, summer upgrades, testing phase, shrinking windows to validate the functionality like the certain, uh, duration. We need to test our entire, uh, functionalities in those two environments before it goes to the production by adding artificial intelligence and automation into the mix. The challenges was intensify a driven futures like predictive analytics in SAP and. Automate code generation in Salesforce evolve dynamically and making the static steps absolute. This is evident where recent project, where a model adjustment in Salesforce require retesting 70% of the suite stretching the timeline within. And what are the example? We have undergone this one key issues, this lack of comprehensive test coverage with the thousands of testation. With the 5,000 per SAP module, Salesforce, this critical path is prioritizing the critical path is tough complaints requirement like GXP in any medical, medical or formal, critical. Financial, uh, industry stocks demands a hundred percentage accuracy and, uh, the resource constraints often limit the testing for 70, 60 percentage or 70 80 percentage coverage. Leave the risk unadressed on this. So what can we do? The first step is this one adapting the intelligent testing framework. The tools like toca present, toca, which I have used, automate the regression suites, can handle the multi modules in SAP, the reduce the 50 percentage of efforts, RP parts, other tools which is implemented. Automate the reputative validation, ensure our consistency in financial framework. The second thing, this one, investing the robotic test data management. This test data. Can mimic the production safely on this. And that is the shift left testing catches the defects, a lean CSCD pipeline to keep the face up the updates. It is not only the about the tool, it's a strategy that team needs to be trying to understand. These platforms must be ed, the core investment, not on a after through the difficulty. Engaging 30 percentage may prove very costly after go live on the S. So by going through this one E complexity, land complexity landscape and traditional testing limitations, the instant to preventive actions, whatever the points we've discussed, it's need to be carefully. We need to address this information will be held on the our building. This one AI and machine learning process. Now, AI and machine learning testing in the core technologies, the predictive analyticals for the risk assessment mission, learning from test optimization, NLPs. How we are going to do on this? So how do we address these challenges in the real time? So everyone, the usage of this one, this core technologies is very, but let me put in article B, how we can address these challenges either entering a mission, learning, let's, I mean, uh, with the focus of this predictive anal analyticals for the risk assessment. Machine learning from test optimization and the natural language processing for the requirement analysis. Testing involved for a manual checklist for data-driven sciences and a and machine learning are the for beaten of the resolutions. These technologies are reshaping how ensuring this quality in complex systems like SAPS four HANA and the Salesforce, where error can have far reaching consequences. Let's debr one by one First, predictive analytical for the risk assessment. This technology using a historical data, it could be the defect data or the issues in the production environment, which we coming across. And AA modules to forecast where the defects are likely to be occur. Imagine you are, you're doing SAPS four HANA upgrade or rollout with hundreds of modules and tracks, T cores, where single update could be triggered. The cascade of issues like a price example, price issue, or currency issue. The predictive analyticals trained in the on the past defect patterns can prioritize the high risk areas like complaints, critical workflows under JXP SOS FDA regulations part level. In my work, I have been seeing this action, a predictive model flagged for potential failures, a enhanced that. Sales and distribution module and inventory module to reduce the production deployment at least for 25 to 30 percentage during a recent upgrade. The next one, machine learning and test optimization. This approach leverages the algorithm to refine the test suites by eliminating the redundant and focus what it's matter. Tradition testing after involves how and of test cases saying this one, take this one. S 400,000 test cases. Salesforce thousand test cases may have char the outdated data updates, machine learning, analyze the ex execution results to identify the most. Impactful testing, cutting efforts by 40 percentage. As we discussed earlier, when the changes comes from S four, we are going to read the one, the changes about which object is impacting. Similarly in Salesforce. We are going to read out on this one, what is the change summary? Our pull request is what the score change for by reading this one. When we implement mission learning driven tool that optimize your regression testing, cutting the unnecessary work, at least for 40 to 60 percentage. On top of this one, they're shortening the test cycle from weeks to days while maintaining the coverage. This is not only saves the time, but also adapt the dynamic nature of aid driven future. Finally, the NLP that we call is a natural language processing for requirement analysis, NLP, and interprets the requirement from unstructured text, email documents, user stories from xera or any, any pa any format turning in inputs into pre, uh, six or, uh, test cases, sorry, precious test cases. The, the requirement. You can say this one, there is a blueprint or FTDs function. Documents or technical requirement documents or functional requirement documents or specs, any format in this. It'll read this one data and it'll be convert into this one into precise test cases in Salesforce CPQ project, where the client requirements then evolved. NLP used 50 pages of specs, either functional technical specs or user stories. Identify this key test scenarios to reduce the at least by 32. For 30 to 50%. I've used this in my career to analyze the SAP notes, GitHub pull request transports a live compare reports or the objects list. Ensuring this one we have have a clearly articulated our test cases. Along with this one, the evident of test conditions. This technology is crucial for managing the complex AI requirement where intent context matters as much as code. Let's share the real world example during S four ANA implementation prior to analyticals. Identify the high risk areas like a global trade system co due to a modular adjustment by focusing the testing. There we have ed, potential regulator, beaches. Um. The machine learning also, meanwhile, the machine learning optimized the test speed cutting down the return test cases. NLP clarified the requirement from 20 pages change log, ensuring this one we have the full coverage. This combination prevented from delay that could have the cost of millions. So, but how does this technology work? Together we are talking about the predictive analyticals pinpoint the risk mission learning, streamline execution, NLP, ensuring the alignment with the business needs. Together they're creating the proactive testing ecosystem ideal for AA enhanced platform, where the tradition method will the challenges. The integration of this tool required the skill team to robust the data pipeline. But the pace off is clear. The faster releases, uh, fewer defects and greater confluence. What next? Start by adapting this technology increment, the, perhaps a pilot with the predictive analyticals for your next SAP updates, a power testing strategies in SAP environment. We are going to break down this one, our strategies, uh, ASAP as well as Salesforce. First, let's, we'll go with the focus on SAP environment. Uh. So when we are focusing with this one intelligent module with integration testing, automate the con customization, impact analysis, performance, scalability, predictions. As we discussed, S four is a very critical, powerful business functions we have with the complex integrations, frequent updates, the tradition techniques after fails, a shot, a stepping it transform the landscape, offering the targeted strategies to ensure this one reliability and efficiency. Let's this have a three key approaches here. First, intelligent module integrations. This strategy uses a, a two. Validate interaction across the, all the SAP models where change is one, like updating SD can impact MM. Or finance a e. A agent analyze the data flow and predict the integration points, reduce the manual efforts. We implemented by using a tool, Tosca Tic Tosca and RPA, where an A module tested between SD and finance integration and SDN Mm integration for a inventory system. It caught the data mismatch during the pilot and preventing the. Post-launch failure and saves the rework as well. In the financial point of view, we, we have caught the defect on the group currencies and the conversions and also the reports. We, we found the defects that was helped us to avoid the rework, the production environment. The next one, the automate the customization, impact analysis. SAP environments, often future custom codes. Think pricing logic complaints as rules for air socks and JXPA automates the assessment, how this customization reacts for updates and transport. A recent project, we've used a technology to analyze this one for live compare data. Identify this one, what the changes on this, how this going to affect in our current business process, and how many test cases will be impacted. So we predict this one and we packaged by doing this one upfront itself. The regression team was saved 35 percentage of testing time by predicting and doing the early work on this. Finally, the performance and scalability prediction. This leverage AI to simulate the load, predict system behavior under stress. Critical for SA real time demands. The tradition testing teams might miss how an AI enhanced SCM module or CRM module handles the peak traffic. We deployed Predict Q Model that forecast the performance during the high value with the maximum load, and we are revealing the scalability of bottlenecks and adjusting our configuration. Pre-launch AWAI 20 percentage slowdown by proving this AI value, maintaining a up time. Let's, we have some real time example here. AA predicted demand data in a CM conflict with, uh. Stocks level. Automated customization impact analysis that flagged the custom GXP complaints as scripted need to be adjusted. While performance and scalability prediction ensure the system handle the 50 to 60% of traffic spike together, the strategy shaved together. This strategies shaved up two weeks of testing cycle and ensure this one flawless go lie. Why this matter? C'S complexity, as we mentioned, and the thousands of transactions and few F print updates, a integrations demands the precise testing, the A power test strategies. Reduce the risk, optimize the resource, align with the business goals, the change, the integration. AI required robust data, the FA pay off the fewer defects, faster deployments. Now let's we go back to the Salesforce specific, a testing approach. There are three trailer made approach. Stand out Salesforce specific, a testing approach, focusing on declarative configuration validation, third party testing and user experience impact prediction. The Salesforce power custom centric operations worldwide from CPQ to multi-cloud deployments and the integration with ai transforming how we can work. But this is brings unique testing challenges. Tradition methods often miss the issues, but by using a aid driven future, let's explore this targeted strategies to ensure the quality and reliability. The first is declarative configuration validations. The Salesforce declarative tools flows, workflows, and the validation enables the rapid customization. Often enhanced by AI for automation. AI testing validates this configuration for accuracy edge cases. I used a power tool to test the CPQ pricing flow where a module adjusted discount dramatically. And, uh, the, when I said this one dramatically or the based on the country, based on the customer, the setup, the price dynamically, uh, changes. And moreover, in the when the real time to transaction is cha. Uh, when we are real time when trying to create the order in the backend by user, any user has been changing this one validations that dramatically the system will choose, um, review on that and immediately the system will dynamically update this one. So we brought, we brought that situation. Where's the scenario? And simultaneously we are checking this one, the how this AA can adjust on this, this validation caught a logical error. Sometimes the word is going of. Some, uh, 15 to 20 percentage. We, we have prevented a revenue losses of thousand during this one piloted. So this approach ensure this one configuration aligned with the business rules even adapt, EY and AI adapt stem. Next third party integration testing Salesforce after integrated with the external systems with SAP or payment gateways or marketing platforms where AI enhanced the data, syncing our decision making AI testing simulate this interaction, detecting the failures at the boundaries. In project. At, in project I implemented air driven testing for the Salesforce. Uh. PAP integration where a agent predicted data inconsistency during the peak loads. It's identifies a sync delay allowing us to adjust the EPA calls frequency before go live, the awaiting the customer facing issues. And finally, the user experience impact predictions. A predicts how changes new future a enhancements are. Update after an end user from sales representative to customers using machine learning modules. It is analyzed the user patents and simulate this one. UI impacts. I deployed this to access the access, the Salesforce CPQ updates with the A driven code generator, the prediction flag with the uua lag for mobile user prompting, the redesigning the improved customer satisfaction scoring by 22 30% edge post launch. This ensure this one, the seamless experience in a augmented environment. Let's share some live example here during, uh, rollouts declarative configuration validation. Ensure this one, the pricing rules worked across the region, catching the currency conversion glitch. Third party integration testing, validate the payment gateway link and preventing the transaction failures during a high volume sales user experience. Impact predictions, conforming that updates did not slow down in the. Sales team. This leading a successful launch of zero deployment complaints. Why this matter? The Salesforce flexibility, there's thousands of configurations and integrations and user touch plan demands. These AI approaches reduce the risk, optimize the deployment, enhance the user satisfaction. This challenges lies in the data quality and tool integration. This benefits the few defects, faster releases, implementing the framework, building a powered testing capabilities. Let's outline the framework. Let's start with this one. Assessment and planning phase. Evaluate the existing. Testing capabilities, infrastructure readiness and organization maturity. Only the stakeholders in objective and success metrics. Ensuring this one system support the data needs the next technology stack. Tool selection, excuse me. Then data strategy, model development, developing the strategies for data collection, storing while maintaining the security using interactive, excuse me, for the continuous refinement. Finally, the integration with the existing workflows. Ensuring a tool will measure the seamless with CACD pipeline test management. Sorry, the assessment of, uh, planning phase. So let, let, let's walk through framework step by step first, as we discussed at the beginning, assessment of planning phase, what is this? What it does? Actually, this evaluates your current testing landscapes and the process skill gaps and identify the pain point, sorry. Identify the pain points like manual coverage gaps, slow regression cycles. I lead the assessment for the S four and uncover 60% of test cases where. Redundant due to outdated scripts. This phase let's goals says, reduce the defects by 30 percentage. Build a roadmap for the business objects, for the complaints, SOX or gxp, FDA and speed. The next data strategies module, development A. A thrives on the quality data. So the steps mainly focus on gathering and preparing the data sets, test results, defects, logs, user behavior, developing the AM modules, trial and made to your needs, like ing the risk and optimizing the test cases. We designed a module using a historical SAPS four, as well as Salesforce data to forecast defect prone areas, improving the test focus by 30 to 40% day. This phase requires a clean and secure interactive mod to reflect the real world scenarios. The third one is this one, the technology stack and tool selection, choosing the AI tool fit your ecosystem, and we selected few projects, toka ml, FAT M two, enhance SAP testing, cutting the execution time. We did this one and the scalability of future growth on that. Finally, the integration with the existing close. This step embedded a testing in CACD pipeline Q processing and the team practice the seamless integrations, which is reduce the DF 20 percentage defect. Quarterly proving of this, you can ask me this one. Why does this framework matters? Building a power testing is one of the project. It is a transformation. Without assessment, your risk mis, uh, misaligned goals without data and models a lack accuracy. Without the right tool, scalability suffers, and without integration adaption styles. The challenge is this one, balancing investment with the results, but the payoff, faster, smarter testing and and level. How do we start? Begin with this one. Some pilot project assessment phase, focus on single model. It could be the S-D-R-M-M or finance. Build a data strategy, the accessible test data, select the tool and integrate increment incrementally. The this phase approach cut down the data leakage. 2235 percentage showing a major success. The technical implementation requirement during the technical implementation needs the, the robust, robust data, the key types including, uh, historical test results production. Logs, user behavior data and the configuration. High quality drives data is crucial for effective module training only. Deduction data preparation, cleaning, labeling sensitive information ensure our compliance model, integrity. The foundation strategies paramount for a L learning capabilities. Operationally, the seamless integration, which is key with the C-E-C-A-D, sorry, with CACD pipelines, the test tool, tool management, like you can see zero or Q test and automation Framework. Framework like a selenium is critical. Scalability and performance and data security is vital, especially for the large system like SAP and Salesforce. Adhering to regulator like GDPR must be core part of the operation framework. Technical deep, A testing architecture. Let's take a deep look into this one architecture. The core, a engine has a three component. One is data registration layer collects cell logs, performance metrics, the machine learning engine process, the data testing intelligence, the decision, eh. Uh, sorry. The decision engine commands the insights and generate the actionable recommendation based on the business practice, the integration. API first designed the flexible tool chain and even driven the architecture for the real time response view with the bi directions by enabling the backbone integration. So the main thing, this one here, we, we are doing on this one. The scalability relies on the distributing processing of the various demands, catching the performance, multi modules tell the business what they need from this. So the main thing, this one in SAP and Salesforce, the core A engines where you collect the information that's based on that, we, we are generating the predictive analysis that will enhance using this machine learning. We process this one, that algorithm that makes this one rule based decision that the core components will go into on that. From there, we are going to integrate this one architecture platform. Then the scalability concentration will come risk based testing and, uh, predictive and intelligent risk assessment, predictive failure analysis, dynamic test. Again, this will vary based on the, which project you're working for, which your client you're working. Intelligent risk assessment model. Use that AI to access the failure properties. Again, failure probability, it'll come. Considering this one, the what data you've been fitted concerning that complex business process. Like each, as I mentioned this one, each application, each business process critical. And how you customize by predicting failure analysis enable the oration, forcing the failures modules by analyzing the code changes, integration patterns. So very important how you are feeding this one, uh, predictive analytics. Based on that system start reacting. Dynamic test A are just the testing focus in the real time based on the risk and business needs too. Ensuring this one, you have this one given this one, right? Uh, impact areas and also the mission learning, especially the corrections we are naming, enabling the practice. Interventions. This will truly chain your, truly will enhance your ai. How you are building on this? So as I told you this one, the based on your, the predictive data, which is how you feed in, uh, how you feed your, uh, system. Based on that, the algorithm we need to build based on that, it will be paid off by cutting the defect much earlier in the production system before, uh, going to the production system, automate the test generation and smart selection. Let's talk about the automation here. A driven test case creates the creation, uses the machine learning, analyze this one, system specs, user behavior, generating the comprehensive test cases or standard and edge scenarios. So NLP translates the requirement to ex executable, at least for 30 to 40 percentage. We need to review and we need to adjust this one, our rules and we need to review whatever the, uh, test intelligent tested, are generated. Put this one the test conditions and feed our, um, automated, uh, NLP. And based on that, whatever the data you are getting, um, make sure this one that is being maintained in very sensitive informa. I mean since to be, um, the ensuring this one, the integrated works well, the adapt to test, uh, suite optimization, continuously defined suite by identifying the redundant test cases, coverage gaps, and generating the additional test cases as we need. And, uh, this, again, this is very continuous program. The system is key on changes. The new rules will come, some of them business process will going to be updated. So this is a continuous implement activity where you have to make this one automate test case generation and smart selection simultaneously. You're going to bring this money, your regulation, library, new cases. At the same time. You have to make sure this one, some of the uh, uh, the test cases, which is not, uh, not u currently not in the business process and then not, not useful for, to identify the defect on that. So you have to enhance this one. Then only your AA will shine on this self, self-healing systems track to monitoring on this here, here where, uh, where it's exactly, um, I mean, uh, here where I'm exceeded here, there's self-healing systems. So autonomously, uh, issue resolution uses the machine learning to identify the issue, and we are preventing the corrections, uh, uh, by using a AA vision, detecting the deviation with that signal problem. The continuous validation and monitoring prevents the ongoing health. Catching this one, the issues. So when, and this is usually used to come across this one ey. Like whenever this one, the position of this one, the fearless lens has been changed, our self feeling system with a vision, it'll go and it'll be to the corrective adjustments. And so that, um, without your communicating the system, system will, we have driven the rules. And based on that, uh, it'll be, it'll go and correct this one and wait for our confirmation once we confirm on this one. And it'll be implemented in this one irrigation library. So we made the checklist before implementing. The one of our review will come on. This one, and I'll confirm on this, is again, feedback loop integration enabled continuous learning, analyzing the test effectiveness, instant prevent success. This is how we are preventing even the instance in the fu um, production system. This is applicable for both SPO as well as, uh, uh. Salesforce integration with the CSCD pipeline. Um, as we discussed earlier, the seamless pipeline integration leverage the existing architecture tool, standardizing a PA flexible across the architecture, the real time the quality gates makes the intelligent deployment decision based on the test results and the risk assessment. Uh, mainly we are covering the business criticality. The automated rollback decision monitors the post deployment metrics and triggering the rollbacks and the instant, the safe way decision aggregate. Um, I mean the safe net supports the aggressive deployment while maintain the reliability on this, this main thing is this one seamlessly. How you're integrating on that, how you maintain the real time quality gate and, uh, automate the roll practition making on anything, uh, that safely we can maintain the deploys. Measuring success and, uh, me, uh, metrics and ROI, how do we know it is working? Measuring success involves the quantity to metrics improve the test coverage early, um, defect, uh, detections, reducing the testing time and cycles, fewer deployment issues and co cost savings. Quant, uh, quality to and quantitative we see enhance, uh, developer experience better per quality, increase team confidence, improve the risk management. Long-term value includes the faster innovations market, competitive customer satisfaction, and up to new, new technologies. This metrics proved the return on investment of a power testing. Here we go. The future of enterprise testing. Looking ahead, AI testing is shifting us from reactor to predictive approach. Advanced mission learning like deep learning, will detect the substant failure patterns while cloud navigate the testing with a scale and microservices emerging integrations patterns. API first design, event driven architecture and low code platforms will bring the new challenges and opportunities Organization adapts. This capabilities will gain the competitive advantage in the reliability and deliverable. The future is bright. I would like to. Tell you this brings us Anna end. Thank you so much for your attention. I hope you gain valuable insights, how AA can prevent the instance in S four SAP Anna, Salesforce environment. I'm happy to take the questions, but as this is online, I'm stopping here. Thank you everyone.