CarParts experienced a surge in demand during the COVID-19 pandemic but has since seen its share price decline. Learn why I'm neutral on PRTS stock.

CarParts: Undervalued For A Reason, Initiate At Neutral

Customers can purchase online high-quality automotive parts and accessories at competitive prices and have them shipped directly to their homes

AutoNation Launches AutoNationParts.com

For more Miami community news, look no further than Miami Community Newspapers. This Miami online group of newspapers covers a variety of topics about the local community and beyond. Miami’s Community Newspapers offers daily news, online resources ...

Grant Miller and Obie visit Autonation to test drive some amazing cars!

According to market intelligence firm Mintel, consumers are increasingly shopping online for auto parts ... A Fortune 200© Company with the nation's most J.D. Power Dealers of Excellence, AutoNation is transforming the automotive retail business by ...

Autonation Launches Autonationparts

Find the latest AutoNation, Inc. (AN) stock forecast based on top analyst's estimates, plus more investing and trading data from Yahoo Finance

AutoNation, Inc.: Maintaining BUY following 3Q results

Fintel reports that on October 31, 2023, JP Morgan upgraded their outlook for Autonation (NYSE:AN) from Underweight to Neutral . Analyst Price Forecast Suggests 39.52% Upside As of October 5, 2023, the average one-year price target for Autonation is 180.

JP Morgan Upgrades Autonation (AN)

AutoNation Inc (NYSE:AN) stock is up 1.3% to trade at $130.90 at last check, after J.P. Morgan Securities just upgraded the auto retailer to "neutral" from "underweight," with a price-target cut to $145 from $150, citing the company's valuation. Stephens also threw in an adjustment, lowering AN's price target to $152 from $155.

Analyst Unfazed by AutoNation Stock Struggles

The idled Belvidere Auto Assembly Plant could have new life under a new contract negotiated between Jeep maker Stellantis and the United Auto Workers (UAW) union.

New UAW contract could bring Belvidere Assembly Plant out of idled state

How will the United Auto Workers agreements impact car sales? What happens next with United Auto Workers union?

United Auto Workers reach tentative deal with GM. What happens next?

The United Auto Workers union says it reached a tentative contract with General Motors, the last of the Detroit Three automakers to agree to a deal. Under the deal reached

UAW reaches deal with General Motors that ends strikes against Detroit automakers pending votes

AutoNation

autonation

U.S. Supreme Court Justice Clarence Thomas and His Powerful Billionaire Friends

মার্কিন সুপ্রিম কোর্টের বিচারপতি ক্লারেন্স থমাস এবং তার শক্তিশালী বিলিয়নেয়ার বন্ধুরা

Clarence Thomas, Richter am Obersten Gerichtshof der USA, und seine mächtigen milliardenschweren Freunde

El juez de la Corte Suprema de Estados Unidos, Clarence Thomas, y sus poderosos amigos multimillonarios

Clarence Thomas, juge de la Cour suprême des États-Unis, et ses puissants amis milliardaires

अमेरिकी सुप्रीम कोर्ट के न्यायाधीश क्लेरेंस थॉमस और उनके शक्तिशाली अरबपति मित्र

米国最高裁判所判事クラレンス・トーマスと彼の強力な億万長者の友人たち

미국 대법원 판사 클라렌스 토마스(Clarence Thomas)와 그의 막강한 억만장자 친구들

O juiz da Suprema Corte dos EUA, Clarence Thomas, e seus poderosos amigos bilionários

Судья Верховного суда США Кларенс Томас и его влиятельные друзья-миллиардеры

ABD Yüksek Mahkemesi Yargıcı Clarence Thomas ve Güçlü Milyarder Arkadaşları

Thẩm phán Tòa án Tối cao Hoa Kỳ Clarence Thomas và những người bạn tỷ phú đầy quyền lực của ông

美国最高法院法官克拉伦斯·托马斯和他有权势的亿万富翁朋友

AI is one of the most game-changing tech innovations today improving business operations across different industries. Bill Gates stated in his blog that the emergence of AI was as significant as the establishment of the Internet. Businesses worldwide are increasingly embracing innovation through the power of AI. Siemens, a global technology powerhouse, leverages AI to boost operational efficiency by advancing coordination and time management, refining malfunction identification, and stabilizing production procedures. Rolls-Royce, an engineering company specializing in automobiles and aerospace technologies, together with Singapore’s DSTA, developed aviation upkeep systems (digital twins) that use machine vision to reach hard-to-access engine parts and detect probable issues before they lead to costly repairs or disruptions. KFC, in partnership with Baidu, introduced a regional facial recognition system that evaluates age, gender, and facial expressions and recommends menu items that align with the customer’s mood and tastes. So, it should be no surprise that QA and software testing are no exception. According to the World Quality Report 2024-25 (WQR), 34% of respondents are already actively utilizing Gen AI in their quality engineering processes. Even with the complexities and considerable concerns surrounding AI implementation, its combination with test automation can potentially bring various advantages for businesses. In this article, let’s see what prospective advantages companies can derive from AI-driven test automation, what hurdles they can confront, and the use cases of applying this technology. Unlocking the Full Potential of AI-driven Test Automation The WQR interviewees mentioned that they expect test automation to minimize various risks (54%), decrease the number of live issues (51%), and improve customer experience (50%). By strengthening it with AI, businesses can: Maximize velocity. AI-driven automated testing allows project teams to swiftly create tests and fix software issues, thus releasing and upgrading high-quality digital solutions faster meeting changing end-user preferences or market requirements. Expand coverage. AI enables QA specialists to write more scripts in less time and find unconventional scenarios that can be missed by humans. This helps cover more aspects and rectify issues before they escalate into full-fledged problems in the production environment. Effectively maintain tests. QA teams can spend less effort on updating tests. AI has the capability to analyze how the code of recently added software features can affect the code of current automated scripts and automatically provide smart alterations. Apply codeless opportunities. Nowadays, teams can choose from a variety of AI-driven codeless platforms that save time on manually writing scripts and ensure higher process efficiency. Lower expenditure. Swifter automated defect scanning and adoption of low/no-code solutions can allow companies to economize financial assets and redistribute available resources on other significant project activities. Improve reporting. AI processes vast amounts of data to generate insightful, visually appealing reports. They offer a clearer understanding of test failure causes and empower project stakeholders to make more informed decisions for enhancing software quality. Analyzing Two Sides of a Coin: Hurdles to Overcome Although AI can enhance the capabilities of automated testing, organizations should remember that its implementation isn’t always smooth sailing, meaning the following roadblocks can occur: Initial implementation expenditure. Before reaping the fruits, companies need to invest in QA professionals who are able to master new technology and learn quickly.
Shortage of expertise. The WQR states that lack of skills (30% of respondents) remains a significant test automation challenge, which means that integration of AI and specific tools can pose even more complexities.
Issues with data. To ensure that AI algorithms operate efficiently, companies should leverage only high-quality data that has no duplicates, discrepancies, or bias. Otherwise, test results may be inaccurate (e.g., false negatives).
Integration problems. Without a detailed preliminary strategy, embedding AI into automated QA workflows may be tricky, which can constrain its potential and hinder probable benefits. Top 5 AI Use Cases for Enhancing Traditional Test Automation Let’s analyze some potential cases of harnessing AI to facilitate more efficient operations. Improved regression testing Regression testing ensures that newly introduced software features do not disrupt existing functionality. As the project evolves and the codebase grows, it becomes increasingly challenging for QA automation engineers to maintain comprehensive test coverage and ensure the reliability of the IT product. Fortunately, AI has the capabilities to swiftly examine previous code alterations and testing outcomes to identify areas most prone to defects and suggest what verifications should be executed first. This helps overcome lackluster productivity and speed up software rollout. More effective GUI verifications First impressions do matter. Studies show that it takes only 50 ms for the target audience to decide whether they like your IT solution or not, while other research shows that 94% of reasons for rejecting a website were related to issues with design. QA automation engineers can enhance their user interface testing capabilities with the help of AI. It allows them to automate the process of verifying UI elements with the help of image-based testing, which improves overall velocity and accuracy. Next-generation scripts AI can automatically and quickly generate intelligent scripts, ensuring a more effective quality control process. AI gathers data on how users work with the IT product and builds predictions on the expected outcomes. In case of any abnormal situations, AI highlights probable issues. Then, QA specialists analyze these messages to identify further actions - opening a defect or continuing testing. Automated recovery opportunities Due to AI’s self-healing possibilities, QA automation engineers can have less work with maintaining and updating scripts after code changes in the application. AI-driven automated scripts can adapt leveraged locators to alterations in the UI to ensure continuous test execution. This helps enhance the robustness of scripts, ease maintenance, prevent disruptions in the testing process, and dedicate more effort to performing other vital QA activities. Better approach for working with issues When AI supplements test automation, it can review previous application-related data and consider software intricacy. This helps to forecast areas where potential issues may arise, allowing QA specialists to focus more on risky software aspects. In addition, companies can leverage test automation frameworks with AI elements. They serve to better understand the reasons for diverse glitches, which makes it easier and faster for QA and development teams to mitigate them. All Things Considered By leveraging AI-empowered test automation, QA teams can potentially boost the productivity, precision, and velocity of testing workflows. Although AI’s introduction is associated with hurdles, this technology can facilitate regression and GUI testing, as well as provide effective self-learning, self-healing, predictive, and cause analysis opportunities. AI is one of the most game-changing tech innovations today improving business operations across different industries. Bill Gates stated in his blog that the emergence of AI was as significant as the establishment of the Internet. blog Businesses worldwide are increasingly embracing innovation through the power of AI . Siemens, a global technology powerhouse, leverages AI to boost operational efficiency by advancing coordination and time management, refining malfunction identification, and stabilizing production procedures. AI leverages AI Rolls-Royce, an engineering company specializing in automobiles and aerospace technologies, together with Singapore’s DSTA, developed aviation upkeep systems (digital twins) that use machine vision to reach hard-to-access engine parts and detect probable issues before they lead to costly repairs or disruptions. machine vision KFC, in partnership with Baidu, introduced a regional facial recognition system that evaluates age, gender, and facial expressions and recommends menu items that align with the customer’s mood and tastes. system So, it should be no surprise that QA and software testing are no exception. According to the World Quality Report 2024-25 (WQR), 34% of respondents are already actively utilizing Gen AI in their quality engineering processes. Even with the complexities and considerable concerns surrounding AI implementation, its combination with test automation can potentially bring various advantages for businesses. In this article, let’s see what prospective advantages companies can derive from AI-driven test automation, what hurdles they can confront, and the use cases of applying this technology. Unlocking the Full Potential of AI-driven Test Automation The WQR interviewees mentioned that they expect test automation to minimize various risks (54%), decrease the number of live issues (51%), and improve customer experience (50%). By strengthening it with AI, businesses can: Maximize velocity. AI-driven automated testing allows project teams to swiftly create tests and fix software issues, thus releasing and upgrading high-quality digital solutions faster meeting changing end-user preferences or market requirements. Maximize velocity. AI-driven automated testing allows project teams to swiftly create tests and fix software issues, thus releasing and upgrading high-quality digital solutions faster meeting changing end-user preferences or market requirements. Expand coverage. AI enables QA specialists to write more scripts in less time and find unconventional scenarios that can be missed by humans. This helps cover more aspects and rectify issues before they escalate into full-fledged problems in the production environment. Expand coverage. AI enables QA specialists to write more scripts in less time and find unconventional scenarios that can be missed by humans. This helps cover more aspects and rectify issues before they escalate into full-fledged problems in the production environment. Effectively maintain tests. QA teams can spend less effort on updating tests. AI has the capability to analyze how the code of recently added software features can affect the code of current automated scripts and automatically provide smart alterations. Effectively maintain tests. QA teams can spend less effort on updating tests. AI has the capability to analyze how the code of recently added software features can affect the code of current automated scripts and automatically provide smart alterations. Apply codeless opportunities. Nowadays, teams can choose from a variety of AI-driven codeless platforms that save time on manually writing scripts and ensure higher process efficiency. Apply codeless opportunities. Nowadays, teams can choose from a variety of AI-driven codeless platforms that save time on manually writing scripts and ensure higher process efficiency. Lower expenditure. Swifter automated defect scanning and adoption of low/no-code solutions can allow companies to economize financial assets and redistribute available resources on other significant project activities. Lower expenditure. Swifter automated defect scanning and adoption of low/no-code solutions can allow companies to economize financial assets and redistribute available resources on other significant project activities. Improve reporting. AI processes vast amounts of data to generate insightful, visually appealing reports. They offer a clearer understanding of test failure causes and empower project stakeholders to make more informed decisions for enhancing software quality. Improve reporting. AI processes vast amounts of data to generate insightful, visually appealing reports. They offer a clearer understanding of test failure causes and empower project stakeholders to make more informed decisions for enhancing software quality. Analyzing Two Sides of a Coin: Hurdles to Overcome Although AI can enhance the capabilities of automated testing, organizations should remember that its implementation isn’t always smooth sailing, meaning the following roadblocks can occur: Initial implementation expenditure. Before reaping the fruits, companies need to invest in QA professionals who are able to master new technology and learn quickly. Shortage of expertise. The WQR states that lack of skills (30% of respondents) remains a significant test automation challenge, which means that integration of AI and specific tools can pose even more complexities. Issues with data. To ensure that AI algorithms operate efficiently, companies should leverage only high-quality data that has no duplicates, discrepancies, or bias. Otherwise, test results may be inaccurate (e.g., false negatives). Integration problems. Without a detailed preliminary strategy, embedding AI into automated QA workflows may be tricky, which can constrain its potential and hinder probable benefits. Initial implementation expenditure. Before reaping the fruits, companies need to invest in QA professionals who are able to master new technology and learn quickly. Shortage of expertise. The WQR states that lack of skills (30% of respondents) remains a significant test automation challenge, which means that integration of AI and specific tools can pose even more complexities. Issues with data. To ensure that AI algorithms operate efficiently, companies should leverage only high-quality data that has no duplicates, discrepancies, or bias. Otherwise, test results may be inaccurate (e.g., false negatives). Integration problems. Without a detailed preliminary strategy, embedding AI into automated QA workflows may be tricky, which can constrain its potential and hinder probable benefits. Top 5 AI Use Cases for Enhancing Traditional Test Automation Let’s analyze some potential cases of harnessing AI to facilitate more efficient operations. Improved regression testing Improved regression testing Regression testing ensures that newly introduced software features do not disrupt existing functionality. As the project evolves and the codebase grows, it becomes increasingly challenging for QA automation engineers to maintain comprehensive test coverage and ensure the reliability of the IT product. Fortunately, AI has the capabilities to swiftly examine previous code alterations and testing outcomes to identify areas most prone to defects and suggest what verifications should be executed first. This helps overcome lackluster productivity and speed up software rollout. More effective GUI verifications More effective GUI verifications First impressions do matter. Studies show that it takes only 50 ms for the target audience to decide whether they like your IT solution or not, while other research shows that 94% of reasons for rejecting a website were related to issues with design. 50 ms 94% QA automation engineers can enhance their user interface testing capabilities with the help of AI. It allows them to automate the process of verifying UI elements with the help of image-based testing, which improves overall velocity and accuracy. Next-generation scripts Next-generation scripts AI can automatically and quickly generate intelligent scripts, ensuring a more effective quality control process. AI gathers data on how users work with the IT product and builds predictions on the expected outcomes. In case of any abnormal situations, AI highlights probable issues. Then, QA specialists analyze these messages to identify further actions - opening a defect or continuing testing. Automated recovery opportunities Automated recovery opportunities Due to AI’s self-healing possibilities, QA automation engineers can have less work with maintaining and updating scripts after code changes in the application. AI-driven automated scripts can adapt leveraged locators to alterations in the UI to ensure continuous test execution. This helps enhance the robustness of scripts, ease maintenance, prevent disruptions in the testing process, and dedicate more effort to performing other vital QA activities. Better approach for working with issues Better approach for working with issues When AI supplements test automation, it can review previous application-related data and consider software intricacy. This helps to forecast areas where potential issues may arise, allowing QA specialists to focus more on risky software aspects. In addition, companies can leverage test automation frameworks with AI elements. They serve to better understand the reasons for diverse glitches, which makes it easier and faster for QA and development teams to mitigate them. All Things Considered By leveraging AI-empowered test automation, QA teams can potentially boost the productivity, precision, and velocity of testing workflows. Although AI’s introduction is associated with hurdles, this technology can facilitate regression and GUI testing, as well as provide effective self-learning, self-healing, predictive, and cause analysis opportunities.

Work Smarter, Not Harder: Using AI to Supercharge Test Automation

Running Cisco pyATS on Windows 11 Helped Me Brush Up My Network Automation Skills

Businesses are racing toward smarter operations through AI, automation, and digital transformation. The challenge doesn’t just lie in the adoption of new tech, however, but in doing so at scale. Mahesh Chayel, a Product Management Leader at Meta, has spent his career bridging complex technologies with tangible business outcomes. With over 12 years of experience leading AI-based products across global giants like TripAdvisor, Oracle, and NEC Asia Pacific, Mahesh’s philosophy on innovation and leadership is rooted in continuous learning, transparency, and mentorship. A Versatile Start in Product Leadership Mahesh’s start in product management wasn’t planned. He began his career as a software engineer at Oracle, where he explored diverse roles, including product marketing, analytics, and investment banking. His pivotal moment came through a mentor’s advice: find what you love, and success will follow. For Mahesh, product management proved to be the ideal fusion of creativity, impact, and strategic thinking—there was something special about creating something that he knows can easily impact people’s daily lives and make a real positive change. A top-ranked graduate from India’s Institute of Management (IIM) and MIT Sloan School of Management, Mahesh excelled in several disciplines. This broad foundation gave him the ability to work seamlessly across industries, a skill that would later prove invaluable in global roles spanning India, Singapore, Japan, and the United States. Innovation with AI at Meta and Beyond At Meta, Mahesh plays a key role in the adoption of generative AI and Machine Learning (ML) solutions. He spearheaded the integration of Gen AI into Ads and Monetization—an initiative designed to level the playing field for small enterprises by enabling them to compete with larger companies. These solutions offer small and medium-sized enterprises the same precision in ad targeting as major corporations. Mahesh’s leadership style centers around clarity and trust. “AI isn’t just about efficiency; it’s about making sure people feel in control of the technology they use,” he explains. During his tenure at TripAdvisor, Mahesh introduced ML-driven advertiser bidding, which empowered smaller businesses to optimize their ad spend and boost their return on investment. This fierce commitment to transparency remains a guiding principle in Mahesh’s work at Meta, where establishing trust in AI products is paramount. Challenges in Scaling and Leading in AI Solutions While AI offers transformative potential, scaling these technologies presents unique challenges. Businesses often struggle with aligning AI initiatives across business units, while smaller organizations need low-barrier solutions to unlock automation. Mahesh’s approach to scaling—what he calls a ‘1-to-100’ model—focuses on optimizing proven systems rather than experimenting from scratch. Mahesh emphasizes that AI must change behaviors, not just processes. “Many organizations try to bolt AI onto their workflows without realizing it requires a shift in mindset,” he notes. His experience implementing scalable automation has made him a sought-after speaker at conferences, including the Product Innovation Summit and the MIT India Summit. Mahesh has had to work harder than most to earn recognition in competitive environments. Growing up in a modest middle-class household in India, he initially struggled to find his calling. “People say you need to know what you want early on, but that wasn’t my journey,” he reflects. His experience of excelling across multiple fields—from engineering to marketing—shaped his ability to adapt quickly. These challenges honed his resilience and helped him approach new problems with confidence. This outlook helped Mahesh inspire others to embrace a mindset of growth through exploration. Mentorship in Community Engagement In addition to leading AI initiatives, Mahesh actively mentors aspiring product managers both within Meta and the broader tech community. Whether through LinkedIn or industry events, he imparts practical advice on identifying product-market fit, scaling solutions, and leading complex teams. “Mentorship is about giving sound advice, but it’s also about helping people discover their potential,” he shares. Mahesh has also spoken at prestigious events such as Product-Led Growth and the Singapore International Energy Symposium, covering topics like building scalable ML products and setting new industry standards for AI-enabled enterprises. For Mahesh, knowledge must be shared to drive collective growth. Recognitions and Notable Achievements Mahesh’s expertise has earned him numerous awards, including the Globee Gold Award for thought leadership and the Stevie Gold Award for contributions to product management and marketing. He has also received internal awards such as the ‘Get Shit Done’ award at TripAdvisor for driving significant business growth and the Oracle U-ROC award for his exceptional performance. His professional journey is also marked by academic achievements, including ranking among the top 1,000 of 300,000 candidates in India’s IIM entrance exam and earning a second-place university ranking during his undergraduate studies. Passion as the Driving Force Mahesh’s career is an excellent example of the importance of following one’s passion. His early experiences navigating multiple roles helped him discover what he truly enjoyed. “Once you love what you do, it stops being a job,” he reflects, a philosophy that has driven his professional success. This mindset is something Mahesh actively instills in the people he mentors. He encourages them to explore diverse career paths and pursue continuous learning. His belief that passion fuels excellence has become a cornerstone of his leadership style. A Future Shaped by AI and Automation Mahesh is focused on tackling some of the most complex challenges facing the business world. His vision involves not just deploying AI solutions but also creating scalable frameworks that redefine industry standards. “The future belongs to those who continue evolving,” he says. Mahesh’s ability to adapt and learn positions him at the forefront of the tech world as he shapes the future of AI and automation. He envisions a world where automation enables businesses to operate more efficiently while also enhancing human creativity. Through continuous learning and collaboration with experts, he plans to stay ahead of the curve and keep pushing boundaries. __Mahesh Chayel’__s work shows the transformative potential of AI and automation when applied thoughtfully and at scale. His approach, which is centered around mentorship, continuous learning, and trust, offers a roadmap for tech professionals navigating digital transformation. Businesses might continue to grapple with the complexities of AI adoption, but Mahesh is armed with expertise and insights to provide both inspiration and practical solutions. As he says himself, “Setting a new standard for innovation and leadership in tech—that’s the vision, and one worth working toward.” Businesses are racing toward smarter operations through AI, automation, and digital transformation. The challenge doesn’t just lie in the adoption of new tech, however, but in doing so at scale. Mahesh Chayel , a Product Management Leader at Meta, has spent his career bridging complex technologies with tangible business outcomes. With over 12 years of experience leading AI-based products across global giants like TripAdvisor, Oracle, and NEC Asia Pacific, Mahesh’s philosophy on innovation and leadership is rooted in continuous learning, transparency, and mentorship. Mahesh Chayel Mahesh Chayel A Versatile Start in Product Leadership A Versatile Start in Product Leadership Mahesh’s start in product management wasn’t planned. He began his career as a software engineer at Oracle , where he explored diverse roles, including product marketing, analytics, and investment banking. His pivotal moment came through a mentor’s advice: find what you love, and success will follow. For Mahesh, product management proved to be the ideal fusion of creativity, impact, and strategic thinking—there was something special about creating something that he knows can easily impact people’s daily lives and make a real positive change. Oracle Oracle A top-ranked graduate from India’s Institute of Management (IIM) and MIT Sloan School of Management, Mahesh excelled in several disciplines. This broad foundation gave him the ability to work seamlessly across industries, a skill that would later prove invaluable in global roles spanning India, Singapore, Japan, and the United States. Innovation with AI at Meta and Beyond Innovation with AI at Meta and Beyond At Meta, Mahesh plays a key role in the adoption of generative AI and Machine Learning (ML) solutions. He spearheaded the integration of Gen AI into Ads and Monetization—an initiative designed to level the playing field for small enterprises by enabling them to compete with larger companies. These solutions offer small and medium-sized enterprises the same precision in ad targeting as major corporations. Mahesh’s leadership style centers around clarity and trust. “AI isn’t just about efficiency; it’s about making sure people feel in control of the technology they use,” he explains. During his tenure at TripAdvisor, Mahesh introduced ML-driven advertiser bidding, which empowered smaller businesses to optimize their ad spend and boost their return on investment. This fierce commitment to transparency remains a guiding principle in Mahesh’s work at Meta, where establishing trust in AI products is paramount. Challenges in Scaling and Leading in AI Solutions Challenges in Scaling and Leading in AI Solutions While AI offers transformative potential, scaling these technologies presents unique challenges. Businesses often struggle with aligning AI initiatives across business units, while smaller organizations need low-barrier solutions to unlock automation. Mahesh’s approach to scaling—what he calls a ‘1-to-100’ model—focuses on optimizing proven systems rather than experimenting from scratch. Mahesh emphasizes that AI must change behaviors, not just processes. “Many organizations try to bolt AI onto their workflows without realizing it requires a shift in mindset,” he notes. His experience implementing scalable automation has made him a sought-after speaker at conferences, including the Product Innovation Summit and the MIT India Summit. Mahesh has had to work harder than most to earn recognition in competitive environments. Growing up in a modest middle-class household in India, he initially struggled to find his calling. “People say you need to know what you want early on, but that wasn’t my journey,” he reflects. His experience of excelling across multiple fields—from engineering to marketing—shaped his ability to adapt quickly. These challenges honed his resilience and helped him approach new problems with confidence. This outlook helped Mahesh inspire others to embrace a mindset of growth through exploration. Mentorship in Community Engagement Mentorship in Community Engagement In addition to leading AI initiatives, Mahesh actively mentors aspiring product managers both within Meta and the broader tech community. Whether through LinkedIn or industry events, he imparts practical advice on identifying product-market fit, scaling solutions, and leading complex teams. “Mentorship is about giving sound advice, but it’s also about helping people discover their potential,” he shares. Mahesh has also spoken at prestigious events such as Product-Led Growth and the Singapore International Energy Symposium, covering topics like building scalable ML products and setting new industry standards for AI-enabled enterprises. For Mahesh, knowledge must be shared to drive collective growth. Recognitions and Notable Achievements Recognitions and Notable Achievements Mahesh’s expertise has earned him numerous awards, including the Globee Gold Award for thought leadership and the Stevie Gold Award for contributions to product management and marketing. He has also received internal awards such as the ‘Get Shit Done’ award at TripAdvisor for driving significant business growth and the Oracle U-ROC award for his exceptional performance. Globee Gold Award Globee Gold Award Stevie Gold Award Stevie Gold Award His professional journey is also marked by academic achievements, including ranking among the top 1,000 of 300,000 candidates in India’s IIM entrance exam and earning a second-place university ranking during his undergraduate studies. Passion as the Driving Force Passion as the Driving Force Mahesh’s career is an excellent example of the importance of following one’s passion. His early experiences navigating multiple roles helped him discover what he truly enjoyed. “Once you love what you do, it stops being a job,” he reflects, a philosophy that has driven his professional success. This mindset is something Mahesh actively instills in the people he mentors. He encourages them to explore diverse career paths and pursue continuous learning. His belief that passion fuels excellence has become a cornerstone of his leadership style. A Future Shaped by AI and Automation A Future Shaped by AI and Automation Mahesh is focused on tackling some of the most complex challenges facing the business world. His vision involves not just deploying AI solutions but also creating scalable frameworks that redefine industry standards. “The future belongs to those who continue evolving,” he says. Mahesh’s ability to adapt and learn positions him at the forefront of the tech world as he shapes the future of AI and automation. He envisions a world where automation enables businesses to operate more efficiently while also enhancing human creativity. Through continuous learning and collaboration with experts, he plans to stay ahead of the curve and keep pushing boundaries. __ Mahesh Chayel’ __s work shows the transformative potential of AI and automation when applied thoughtfully and at scale. His approach, which is centered around mentorship, continuous learning, and trust, offers a roadmap for tech professionals navigating digital transformation. Mahesh Chayel’ Businesses might continue to grapple with the complexities of AI adoption, but Mahesh is armed with expertise and insights to provide both inspiration and practical solutions. As he says himself, “Setting a new standard for innovation and leadership in tech—that’s the vision, and one worth working toward.”

Scaling AI and Automation for the Enterprise: Insights from Mahesh Chayel

Škálování umělé inteligence a automatizace pro podniky: Postřehy od Maheshe Chayela

Escalabilidad de la IA y la automatización para la empresa: perspectivas de Mahesh Chayel

Méretezési mesterséges intelligencia és automatizálás a vállalatok számára: Mahesh Chayel betekintése

企業向け AI と自動化の拡張: Mahesh Chayel の洞察

Аж ахуйн нэгжийн хиймэл оюун ухаан ба автоматжуулалтыг өргөжүүлэх: Махеш Чайелаас авсан ойлголтууд

Scalarea inteligenței artificiale și automatizării pentru întreprindere: informații de la Mahesh Chayel

Škálovanie AI a automatizácia pre podnik: Štatistiky od Mahesha Chayela

Kuongeza AI na Uendeshaji Kiotomatiki kwa Biashara: Maarifa kutoka kwa Mahesh Chayel

การปรับขนาด AI และระบบอัตโนมัติสำหรับองค์กร: ข้อมูลเชิงลึกจาก Mahesh Chayel

ስኬሊንግ ኤኣይን ኣውቶማቲክን ንኢንተርፕራይዝ፡ ርድኢታት ካብ ማሄሽ ቻይል

Ölçeg AI we kärhana üçin awtomatlaşdyryş: Maheş Çaýeliň düşündirişleri

Ku ringanisa AI na Otomatiki ya Bindzu: Vutivi ku suka eka Mahesh Chayel

Don't spend your time doing work a well-trained monkey could do | From automate the boring stuff with python book Automation is not a replacement for our workforce; it presents an opportunity to embrace agility in a constantly evolving digital society. Considering network engineers' numerous tedious tasks each week, automation becomes a crucial ingredient for success. Beyond the hype and rankings, Python has emerged as the de facto language for automation. Its elegant syntax and versatility have led to the creation of amazing tools. What is Cisco PyATS pyATS, which stands for Python Automated Test System, was developed by Cisco Systems to automate testing and validate networks. It is often called a "Swiss Army knife" due to its ability to perform a wide range of tasks beyond conventional expectations. In addition to automated testing, pyATS supports network configuration testing and updates,... Language: Python, it is oriented for DevOps and NetDevOps


Typical usage: Network configuration tests, anomaly detection, connectivity validation…


Objective: Automating network tests and validation by providing a modular and extensible environment.
Main modules


PyATS: the main framework for automated tests.


Genie: Collection of automation libraries for configuring, analyzing, and verifying network data. PyATS is a big ecosystem of innovations where we can do amazing and it goes beyond Cisco environments! How to install pyATS quickly on Windows Subsystem for Linux ( Ubuntu 24.04 LTS) The best way to install pyATS is to create a Python virtual environment and this is the quickest way to do it by observing the capture below installing pyATS To install Genie the process is the same! Checking the installation PyATS and Genie are installed with their packages. Verifying PyATS versions pyats version check After running the command we see the version,pyATS is properly installed and we can achieve amazing things we want. Key concepts to know about pyATS What makes pyATS unique is its abstraction, and to understand it better, you need to engage with these abstractions! Testbed: This is the representation of network environments. A YAML file describes devices, their roles, and IP addresses, and here is how it looks when the file is written.
# Step 0: list of devices
devices:
  iosxr1:
    # Step 1: OS and Type
    type: iosxr-Prod
    os: iosxr
    # Step 2: credentials
    credentials:
      default:
        username: admin
        password: Hacker@204k
    # Step 3: connection parameters
    connections:
      vty:
        protocol: ssh
        ip: test-mgmt.talesoftechnology.com
        port: 8080 Test scripts: python scripts that contain all instructions for every test.
Parsers: with an impressive library and over 1500 parsers, Genie expertly converts raw text into structured text.
Triggers: they enable specific automated actions, in response to events, in other words, they are similar to webhooks. Main features and use cases States comparison: network comparison state before and after a change or update.
Report and journaling: it generates test reports and detailed logs for every
Multi-environment execution: it is compatible with physical and virtual environments. Practical use cases State validation: Compare the configuration status of a device before and after a change to ensure there is no configuration drift.
Anomaly detection: Automate the identification of anomalies in device configurations or states.
Connectivity Test: Use ping or traceroute commands to check connectivity between devices Conclusion Now you know how to install pyATS using the Windows Subsystem for Linux, and you don’t need a lengthy guide! You understand the basic concepts; even though pyATS can seem abstract, the best way to grasp it is through interaction with various abstractions. Automation is essential for agility today, you don’t have to reinvent the wheel with tedious tasks. With pyATS, you can save time and   reduce stress compared to handling  everything manually. In the next article, you will learn how to create a testbed, and we will demystify pyATS, it’s not as hard as you think! Don't spend your time doing work a well-trained monkey could do | From automate the boring stuff with python book Don't spend your time doing work a well-trained monkey could do | From automate the boring stuff with python book Automation is not a replacement for our workforce; it presents an opportunity to embrace agility in a constantly evolving digital society. Considering network engineers' numerous tedious tasks each week, automation becomes a crucial ingredient for success. Beyond the hype and rankings, Python has emerged as the de facto language for automation. Its elegant syntax and versatility have led to the creation of amazing tools. Python Python What is Cisco PyATS pyATS , which stands for Python Automated Test System, was developed by Cisco Systems to automate testing and validate networks. It is often called a "Swiss Army knife" due to its ability to perform a wide range of tasks beyond conventional expectations. In addition to automated testing, pyATS supports network configuration testing and updates,... pyATS pyATS Language: Python, it is oriented for DevOps and NetDevOps Typical usage: Network configuration tests, anomaly detection, connectivity validation… Objective: Automating network tests and validation by providing a modular and extensible environment.
Main modules PyATS: the main framework for automated tests. Genie: Collection of automation libraries for configuring, analyzing, and verifying network data. Language: Python, it is oriented for DevOps and NetDevOps Language : Python, it is oriented for DevOps and NetDevOps Language Typical usage: Network configuration tests, anomaly detection, connectivity validation… Typical usage : Network configuration tests, anomaly detection, connectivity validation… Typical usage Objective: Automating network tests and validation by providing a modular and extensible environment. Main modules Objective : Automating network tests and validation by providing a modular and extensible environment. Objective Main modules PyATS: the main framework for automated tests. PyATS : the main framework for automated tests. PyATS Genie: Collection of automation libraries for configuring, analyzing, and verifying network data. Genie : Collection of automation libraries for configuring, analyzing, and verifying network data. Genie PyATS is a big ecosystem of innovations where we can do amazing and it goes beyond Cisco environments! PyATS is a big ecosystem of innovations where we can do amazing and it goes beyond Cisco environments! PyATS is a big ecosystem of innovations where we can do amazing and it goes beyond Cisco environments! How to install pyATS quickly on Windows Subsystem for Linux ( Ubuntu 24.04 LTS) The best way to install pyATS is to create a Python virtual environment and this is the quickest way to do it by observing the capture below installing pyATS To install Genie the process is the same! Genie Genie Checking the installation PyATS and Genie are installed with their packages. Verifying PyATS versions pyats version check pyats version check After running the command we see the version,pyATS is properly installed and we can achieve amazing things we want. Key concepts to know about pyATS What makes pyATS unique is its abstraction, and to understand it better, you need to engage with these abstractions! Testbed: This is the representation of network environments. A YAML file describes devices, their roles, and IP addresses, and here is how it looks when the file is written.
# Step 0: list of devices
devices:
  iosxr1:
    # Step 1: OS and Type
    type: iosxr-Prod
    os: iosxr
    # Step 2: credentials
    credentials:
      default:
        username: admin
        password: Hacker@204k
    # Step 3: connection parameters
    connections:
      vty:
        protocol: ssh
        ip: test-mgmt.talesoftechnology.com
        port: 8080 Testbed: This is the representation of network environments. A YAML file describes devices, their roles, and IP addresses, and here is how it looks when the file is written. # Step 0: list of devices
devices:
  iosxr1:
    # Step 1: OS and Type
    type: iosxr-Prod
    os: iosxr
    # Step 2: credentials
    credentials:
      default:
        username: admin
        password: Hacker@204k
    # Step 3: connection parameters
    connections:
      vty:
        protocol: ssh
        ip: test-mgmt.talesoftechnology.com
        port: 8080 Testbed : This is the representation of network environments. A YAML file describes devices, their roles, and IP addresses, and here is how it looks when the file is written. Testbed # Step 0: list of devices
devices:
  iosxr1:
    # Step 1: OS and Type
    type: iosxr-Prod
    os: iosxr
    # Step 2: credentials
    credentials:
      default:
        username: admin
        password: Hacker@204k
    # Step 3: connection parameters
    connections:
      vty:
        protocol: ssh
        ip: test-mgmt.talesoftechnology.com
        port: 8080 # Step 0: list of devices
devices:
  iosxr1:
    # Step 1: OS and Type
    type: iosxr-Prod
    os: iosxr
    # Step 2: credentials
    credentials:
      default:
        username: admin
        password: Hacker@204k
    # Step 3: connection parameters
    connections:
      vty:
        protocol: ssh
        ip: test-mgmt.talesoftechnology.com
        port: 8080 Test scripts: python scripts that contain all instructions for every test. Parsers: with an impressive library and over 1500 parsers, Genie expertly converts raw text into structured text. Triggers: they enable specific automated actions, in response to events, in other words, they are similar to webhooks. Test scripts : python scripts that contain all instructions for every test. Test scripts Parsers: with an impressive library and over 1500 parsers, Genie expertly converts raw text into structured text. Parsers: Triggers : they enable specific automated actions, in response to events, in other words, they are similar to webhooks. Triggers Main features and use cases States comparison: network comparison state before and after a change or update. Report and journaling: it generates test reports and detailed logs for every Multi-environment execution: it is compatible with physical and virtual environments. States comparison : network comparison state before and after a change or update. States comparison Report and journaling : it generates test reports and detailed logs for every Report and journaling Multi-environment execution : it is compatible with physical and virtual environments. Multi-environment execution Practical use cases State validation: Compare the configuration status of a device before and after a change to ensure there is no configuration drift. Anomaly detection: Automate the identification of anomalies in device configurations or states. Connectivity Test: Use ping or traceroute commands to check connectivity between devices State validation : Compare the configuration status of a device before and after a change to ensure there is no configuration drift. State validation Anomaly detection : Automate the identification of anomalies in device configurations or states. Anomaly detection Connectivity Test : Use ping or traceroute commands to check connectivity between devices Connectivity Test Conclusion Now you know how to install pyATS using the Windows Subsystem for Linux, and you don’t need a lengthy guide! You understand the basic concepts; even though pyATS can seem abstract, the best way to grasp it is through interaction with various abstractions. Automation is essential for agility today, you don’t have to reinvent the wheel with tedious tasks. With pyATS, you can save time and   reduce stress compared to handling  everything manually. In the next article, you will learn how to create a testbed, and we will demystify pyATS, it’s not as hard as you think!

Network Test and Automation: PyATS 101 in 5 Minutes

The TechBeat: How Fintech Is Revolutionizing Compliance: Automation, AI, and the Future of Risk Management  (11/14/2024)

In Part 1 of this series, we went through the process of setting up and installing Appium, along with the necessary dependencies and drivers for mobile automation testing. Now that we have everything installed, let's take a closer look at what exactly we’ve set up and understand the role each component plays in our mobile test automation workflow. Understanding these components will give us a clearer picture of how Appium enables us to automate tests for both Android and iOS applications. 1. Appium Appium is the heart of our mobile automation setup. It acts as a bridge between the test scripts you write and the mobile device or simulator/emulator that the tests are being executed on. Think of Appium as a tool that translates your test scripts (which are written in programming languages like Java, Python, or JavaScript) into commands that the mobile operating system can understand. When we write a test case, Appium takes each action—such as tapping a button, swiping a screen, or entering text—and converts it into a sequence of commands that the mobile OS interprets. Appium is designed to support both Android and iOS, making it a cross-platform solution for mobile test automation. This means that we can write one test and run it across different devices and platforms with minimal changes to our code. 2. UiAutomator Driver The UI Automator driver is a powerful tool for interacting with the UI components on an Android device or emulator. It provides access to all the features needed to automate the interaction with Android app elements. This driver enables actions such as tapping buttons, scrolling through lists, entering text in forms, and verifying the presence of elements on the screen. One key benefit of the UI Automator Driver is its ability to interact with both the app's UI and system-level features, such as accessing notifications and interacting with native Android components. If you’re focusing on testing Android apps, the UI Automator Driver is a critical component of your setup. 3. XCUITest Driver XCUITest is a testing framework developed by Apple for iOS applications. When we need to interact with elements in an iOS app, we use the XCUITest Driver. Just like the UI Automator Driver does for Android, XCUITest translates the actions from your test script into commands that the iOS operating system can execute. XCUITest supports a wide range of iOS-specific features, allowing you to automate gestures, verify UI elements, and even test complex interactions such as push notifications or in-app purchases. Since iOS and Android are very different platforms, the XCUITest Driver provides a tailored approach to testing iOS apps, ensuring that your automation scripts can work seamlessly on Apple's devices. 4. Wait Plugin The Wait Plugin is an essential part of test automation, especially for mobile applications where delays or changes in the UI might cause timing issues. This plugin allows you to pause your test execution until a specific condition is met, such as waiting for an element (like a button or text field) to appear or become clickable. Without such a feature, your tests could fail prematurely, particularly in scenarios where elements load dynamically or after a delay (such as network latency or complex animations). By incorporating the Wait Plugin, you can avoid flaky tests—tests that fail intermittently because the necessary elements haven't loaded in time. The plugin ensures that the test doesn't proceed until it is sure the element is available for interaction, improving the reliability of your automated tests. 5. Appium Inspector Think of Appium Inspector as a visual tool to help you inspect the app's user interface during the test design phase. It provides a graphical interface that displays the app's components, such as buttons, text fields, and images, making it easy to identify elements you want to interact with. You can use the Appium Inspector to locate elements by inspecting their properties like their ID, class, or XPath. This tool is invaluable when designing your test scripts, as it allows you to visually explore the app’s UI and interact with specific elements without writing code immediately. With Appium Inspector, you can quickly determine the exact properties and attributes of UI components, and once you have identified the elements, you can use them in your test scripts to automate interactions. It significantly simplifies the process of writing and debugging automation tests. Conclusion In summary, the components we've installed: Appium, the UI Automator Driver, the XCUITest Driver, the Wait Plugin, and the Appium Inspector, each serve a unique purpose, but they work together to enable powerful mobile automation testing. Appium provides the foundation by connecting the test script with the device, while the drivers for Android and iOS (UI Automator and XCUITest) provide platform-specific functionality. The Wait Plugin ensures reliable test execution by managing timing issues, and the Appium Inspector makes it easier to identify and interact with UI elements. In Part 1 of this series, we went through the process of setting up and installing Appium, along with the necessary dependencies and drivers for mobile automation testing. Now that we have everything installed, let's take a closer look at what exactly we’ve set up and understand the role each component plays in our mobile test automation workflow. Understanding these components will give us a clearer picture of how Appium enables us to automate tests for both Android and iOS applications. Part 1 1. Appium Appium is the heart of our mobile automation setup. It acts as a bridge between the test scripts you write and the mobile device or simulator/emulator that the tests are being executed on. Think of Appium as a tool that translates your test scripts (which are written in programming languages like Java, Python, or JavaScript) into commands that the mobile operating system can understand. When we write a test case, Appium takes each action—such as tapping a button, swiping a screen, or entering text—and converts it into a sequence of commands that the mobile OS interprets. Appium is designed to support both Android and iOS, making it a cross-platform solution for mobile test automation. This means that we can write one test and run it across different devices and platforms with minimal changes to our code. 2. UiAutomator Driver The UI Automator driver is a powerful tool for interacting with the UI components on an Android device or emulator. It provides access to all the features needed to automate the interaction with Android app elements. This driver enables actions such as tapping buttons, scrolling through lists, entering text in forms, and verifying the presence of elements on the screen. One key benefit of the UI Automator Driver is its ability to interact with both the app's UI and system-level features, such as accessing notifications and interacting with native Android components. If you’re focusing on testing Android apps, the UI Automator Driver is a critical component of your setup. 3. XCUITest Driver XCUITest is a testing framework developed by Apple for iOS applications. When we need to interact with elements in an iOS app, we use the XCUITest Driver. Just like the UI Automator Driver does for Android, XCUITest translates the actions from your test script into commands that the iOS operating system can execute. XCUITest supports a wide range of iOS-specific features, allowing you to automate gestures, verify UI elements, and even test complex interactions such as push notifications or in-app purchases. Since iOS and Android are very different platforms, the XCUITest Driver provides a tailored approach to testing iOS apps, ensuring that your automation scripts can work seamlessly on Apple's devices. 4. Wait Plugin The Wait Plugin is an essential part of test automation, especially for mobile applications where delays or changes in the UI might cause timing issues. This plugin allows you to pause your test execution until a specific condition is met, such as waiting for an element (like a button or text field) to appear or become clickable. Without such a feature, your tests could fail prematurely, particularly in scenarios where elements load dynamically or after a delay (such as network latency or complex animations). By incorporating the Wait Plugin, you can avoid flaky tests—tests that fail intermittently because the necessary elements haven't loaded in time. The plugin ensures that the test doesn't proceed until it is sure the element is available for interaction, improving the reliability of your automated tests. 5. Appium Inspector Think of Appium Inspector as a visual tool to help you inspect the app's user interface during the test design phase. It provides a graphical interface that displays the app's components, such as buttons, text fields, and images, making it easy to identify elements you want to interact with. You can use the Appium Inspector to locate elements by inspecting their properties like their ID, class, or XPath. This tool is invaluable when designing your test scripts, as it allows you to visually explore the app’s UI and interact with specific elements without writing code immediately. With Appium Inspector, you can quickly determine the exact properties and attributes of UI components, and once you have identified the elements, you can use them in your test scripts to automate interactions. It significantly simplifies the process of writing and debugging automation tests. Conclusion In summary, the components we've installed: Appium, the UI Automator Driver, the XCUITest Driver, the Wait Plugin, and the Appium Inspector, each serve a unique purpose, but they work together to enable powerful mobile automation testing. Appium provides the foundation by connecting the test script with the device, while the drivers for Android and iOS (UI Automator and XCUITest) provide platform-specific functionality. The Wait Plugin ensures reliable test execution by managing timing issues, and the Appium Inspector makes it easier to identify and interact with UI elements.

Mobile App Test Automation - Getting Started With Appium Part 1B

The TechBeat: How Fintech Is Revolutionizing Compliance: Automation, AI, and the Future of Risk Management  (11/12/2024)

The TechBeat: How Fintech Is Revolutionizing Compliance: Automation, AI, and the Future of Risk Management  (11/10/2024)

In today’s fast-paced financial landscape, staying compliant with ever-evolving regulations is more complex than ever. As fintech continues to reshape the industry, compliance teams are under increasing pressure to manage both global regulatory requirements and the constant threat of cyber risks. With new technologies like AI-powered automation, fintech companies are finding innovative ways to streamline compliance and stay ahead of the curve. Lasma Gavarane, Chief Risk & Compliance Officer at Noda, shares her insights into the key challenges compliance teams face and how fintech is playing a crucial role in transforming regulatory processes. Navigating a Complex Regulatory Landscape One of the biggest challenges for compliance teams today is the sheer volume and speed of regulatory updates. With each country or region introducing its own compliance rules, staying current can be overwhelming for global fintechs. As Gavarane explains: "Navigating rapidly changing regulations across multiple jurisdictions while integrating new technologies is one of the biggest challenges today. The pace of regulatory updates, combined with increasing complexity, stretches resources thin. Add rising cyber threats and the need for robust data privacy protections, and the workload can be overwhelming." Compliance teams must juggle these requirements while ensuring their organizations are up to date with new rules, all while maintaining a focus on security and privacy. Striking a Balance Between Penalties and Innovation There is an ongoing debate about whether tougher penalties for non-compliance are the solution to better regulatory adherence. While penalties do play a crucial role in encouraging compliance, an overly strict approach could hinder innovation, particularly for smaller fintech companies already facing resource constraints. "Regulators should strike a balance between imposing penalties and encouraging proactive compliance. Tougher penalties can deter non-compliance, but overly harsh measures might stifle innovation, especially for smaller fintech firms. Collaboration between regulators and firms, along with incentives for strong compliance practices, fosters a culture of compliance without hindering growth," Gavarane says. Encouraging a culture of compliance, rather than imposing punitive measures, helps fintech companies stay agile and focused on growth while maintaining a strong regulatory framework. The Rise of Regtech: How Technology Is Transforming Compliance Fintech solutions are helping to ease the burden on compliance teams through the rise of "Regtech" (regulatory technology). These AI-powered platforms are capable of automating many of the manual tasks traditionally associated with compliance, such as real-time monitoring, anomaly detection, and adhering to specific regulatory requirements. "Fintech solutions, particularly AI-powered Regtech platforms, are essential in automating compliance processes. These tools enable real-time monitoring, anomaly detection, and adherence to regulatory requirements. By reducing manual workloads, fintech companies can focus more on innovation and customer service," Gavarane highlights. This shift towards automation not only improves efficiency but also ensures that fintechs are staying ahead of their regulatory obligations. The Role of AI in Enhancing Compliance AI is revolutionizing compliance processes by automating time-consuming and resource-heavy tasks. AI-driven systems can quickly process massive amounts of data, identify patterns, and flag suspicious activities in real-time. This not only improves the efficiency of compliance operations but also enhances accuracy, reducing the likelihood of human error. "AI automates routine tasks like transaction monitoring and identity verification. This enhances efficiency and accuracy, while allowing compliance teams to focus on more strategic initiatives," says Gavarane. By automating tasks such as identity verification or transaction monitoring, fintech companies can ensure compliance in a way that is both scalable and efficient, freeing up teams to focus on more complex and critical issues. Risk Orchestration: A New Approach to Emerging Threats In an ever-evolving financial landscape, risk orchestration is becoming increasingly important. Risk orchestration brings together various risk management tools into a unified system, giving companies a complete view of potential threats. This holistic approach allows institutions to react quickly and effectively to any emerging risks. "Risk orchestration enhances agility by providing a comprehensive view of potential threats. This allows institutions to respond swiftly and effectively to emerging risks," Gavarane explains. By using risk orchestration, financial institutions can not only detect potential risks faster but also develop a more proactive approach to managing those risks, ensuring they remain resilient against future challenges. Looking Ahead: The Future of Compliance in Fintech The future of fintech compliance lies in the ongoing adoption of AI and risk management technologies. As regulatory demands continue to evolve, so too must the tools that help businesses navigate these challenges. The integration of AI and Regtech platforms will be key to helping fintech companies maintain compliance without compromising growth. Collaboration between fintechs and regulators will also play an important role in shaping a balanced approach to compliance. By focusing on innovation while maintaining rigorous standards, fintechs can build sustainable business models that not only meet regulatory demands but also set new industry benchmarks. In conclusion, compliance is no longer just about avoiding penalties — it’s about leveraging the latest technologies to build a strong, secure, and adaptable foundation for growth. Through AI, automation, and risk orchestration, fintech companies can streamline their compliance efforts, allowing them to focus on what truly matters: delivering innovative solutions to their customers. In today’s fast-paced financial landscape, staying compliant with ever-evolving regulations is more complex than ever. As fintech continues to reshape the industry, compliance teams are under increasing pressure to manage both global regulatory requirements and the constant threat of cyber risks. With new technologies like AI-powered automation, fintech companies are finding innovative ways to streamline compliance and stay ahead of the curve. Lasma Gavarane, Chief Risk & Compliance Officer at Noda , shares her insights into the key challenges compliance teams face and how fintech is playing a crucial role in transforming regulatory processes. Noda Navigating a Complex Regulatory Landscape One of the biggest challenges for compliance teams today is the sheer volume and speed of regulatory updates. With each country or region introducing its own compliance rules, staying current can be overwhelming for global fintechs. As Gavarane explains: "Navigating rapidly changing regulations across multiple jurisdictions while integrating new technologies is one of the biggest challenges today. The pace of regulatory updates, combined with increasing complexity, stretches resources thin. Add rising cyber threats and the need for robust data privacy protections, and the workload can be overwhelming." "Navigating rapidly changing regulations across multiple jurisdictions while integrating new technologies is one of the biggest challenges today. The pace of regulatory updates, combined with increasing complexity, stretches resources thin. Add rising cyber threats and the need for robust data privacy protections, and the workload can be overwhelming." "Navigating rapidly changing regulations across multiple jurisdictions while integrating new technologies is one of the biggest challenges today. The pace of regulatory updates, combined with increasing complexity, stretches resources thin. Add rising cyber threats and the need for robust data privacy protections, and the workload can be overwhelming." Compliance teams must juggle these requirements while ensuring their organizations are up to date with new rules, all while maintaining a focus on security and privacy. Striking a Balance Between Penalties and Innovation There is an ongoing debate about whether tougher penalties for non-compliance are the solution to better regulatory adherence. While penalties do play a crucial role in encouraging compliance, an overly strict approach could hinder innovation, particularly for smaller fintech companies already facing resource constraints. "Regulators should strike a balance between imposing penalties and encouraging proactive compliance. Tougher penalties can deter non-compliance, but overly harsh measures might stifle innovation, especially for smaller fintech firms. Collaboration between regulators and firms, along with incentives for strong compliance practices, fosters a culture of compliance without hindering growth," Gavarane says. "Regulators should strike a balance between imposing penalties and encouraging proactive compliance. Tougher penalties can deter non-compliance, but overly harsh measures might stifle innovation, especially for smaller fintech firms. Collaboration between regulators and firms, along with incentives for strong compliance practices, fosters a culture of compliance without hindering growth," Gavarane says. "Regulators should strike a balance between imposing penalties and encouraging proactive compliance. Tougher penalties can deter non-compliance, but overly harsh measures might stifle innovation, especially for smaller fintech firms. Collaboration between regulators and firms, along with incentives for strong compliance practices, fosters a culture of compliance without hindering growth," Encouraging a culture of compliance, rather than imposing punitive measures, helps fintech companies stay agile and focused on growth while maintaining a strong regulatory framework. The Rise of Regtech: How Technology Is Transforming Compliance Fintech solutions are helping to ease the burden on compliance teams through the rise of "Regtech" (regulatory technology). These AI-powered platforms are capable of automating many of the manual tasks traditionally associated with compliance, such as real-time monitoring, anomaly detection, and adhering to specific regulatory requirements. "Fintech solutions, particularly AI-powered Regtech platforms, are essential in automating compliance processes. These tools enable real-time monitoring, anomaly detection, and adherence to regulatory requirements. By reducing manual workloads, fintech companies can focus more on innovation and customer service," Gavarane highlights. "Fintech solutions, particularly AI-powered Regtech platforms, are essential in automating compliance processes. These tools enable real-time monitoring, anomaly detection, and adherence to regulatory requirements. By reducing manual workloads, fintech companies can focus more on innovation and customer service," Gavarane highlights. "Fintech solutions, particularly AI-powered Regtech platforms, are essential in automating compliance processes. These tools enable real-time monitoring, anomaly detection, and adherence to regulatory requirements. By reducing manual workloads, fintech companies can focus more on innovation and customer service," This shift towards automation not only improves efficiency but also ensures that fintechs are staying ahead of their regulatory obligations. The Role of AI in Enhancing Compliance AI is revolutionizing compliance processes by automating time-consuming and resource-heavy tasks. AI-driven systems can quickly process massive amounts of data, identify patterns, and flag suspicious activities in real-time. This not only improves the efficiency of compliance operations but also enhances accuracy, reducing the likelihood of human error. "AI automates routine tasks like transaction monitoring and identity verification. This enhances efficiency and accuracy, while allowing compliance teams to focus on more strategic initiatives," says Gavarane. "AI automates routine tasks like transaction monitoring and identity verification. This enhances efficiency and accuracy, while allowing compliance teams to focus on more strategic initiatives," says Gavarane. "AI automates routine tasks like transaction monitoring and identity verification. This enhances efficiency and accuracy, while allowing compliance teams to focus on more strategic initiatives," By automating tasks such as identity verification or transaction monitoring, fintech companies can ensure compliance in a way that is both scalable and efficient, freeing up teams to focus on more complex and critical issues. Risk Orchestration: A New Approach to Emerging Threats In an ever-evolving financial landscape, risk orchestration is becoming increasingly important. Risk orchestration brings together various risk management tools into a unified system, giving companies a complete view of potential threats. This holistic approach allows institutions to react quickly and effectively to any emerging risks. "Risk orchestration enhances agility by providing a comprehensive view of potential threats. This allows institutions to respond swiftly and effectively to emerging risks," Gavarane explains. "Risk orchestration enhances agility by providing a comprehensive view of potential threats. This allows institutions to respond swiftly and effectively to emerging risks," Gavarane explains. "Risk orchestration enhances agility by providing a comprehensive view of potential threats. This allows institutions to respond swiftly and effectively to emerging risks," By using risk orchestration, financial institutions can not only detect potential risks faster but also develop a more proactive approach to managing those risks, ensuring they remain resilient against future challenges. Looking Ahead: The Future of Compliance in Fintech The future of fintech compliance lies in the ongoing adoption of AI and risk management technologies. As regulatory demands continue to evolve, so too must the tools that help businesses navigate these challenges. The integration of AI and Regtech platforms will be key to helping fintech companies maintain compliance without compromising growth. Collaboration between fintechs and regulators will also play an important role in shaping a balanced approach to compliance. By focusing on innovation while maintaining rigorous standards, fintechs can build sustainable business models that not only meet regulatory demands but also set new industry benchmarks. In conclusion, compliance is no longer just about avoiding penalties — it’s about leveraging the latest technologies to build a strong, secure, and adaptable foundation for growth. Through AI, automation, and risk orchestration, fintech companies can streamline their compliance efforts, allowing them to focus on what truly matters: delivering innovative solutions to their customers.

How Fintech Is Revolutionizing Compliance: Automation, AI, and the Future of Risk Management 

Cómo la tecnología financiera está revolucionando el cumplimiento normativo: automatización, inteligencia artificial y el futuro de la gestión de riesgos

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