Practical Applications of AI

AI's hype is everywhere, but its practical application is what truly matters. !! Unlike the self-driving car hype of a decade ago, AI's implementation in the real world is uniquely different. Over the past year, I've witnessed firsthand how AI can augment our capabilities at SJ Innovation. It may not replace our jobs, but it does serve as a powerful assistant, handling numerous tasks efficiently. Since OpenAI introduced the "OpenAI Assistant," we've created over 250 specialized assistants within our organization. Upon reviewing these AI assistants, I've come to realize they haven't replaced any jobs. Instead, they're akin to having a team of interns, each adept at performing specific tasks, saving us 10-15 minutes each time. If you're leveraging 5-10 such assistants, that's a savings of 1-2 hours per day — a significant boost to productivity that will only improve over time. Here are some unusual and small assistant example: 1) Attendance Analysis: Develop AI solutions to analyze attendance data across multiple files, generating comprehensive reports to identify patterns and optimize team schedules. Create and Used by: Admin/Hr department 2) Quality Assurance Report Review: Assist QA teams Assistant manager by tracking project hours versus contracted hours to prevent burnout and ensure optimal productivity. 3) QA/Test cases for Client Project: Upload client project data, past test cases and input new requirements. Result new cases 4) Convert my code to old Version of Cakephp: Client running an application with old version, write code and it convert to old version of cakephp 5) RFP helper: Upload All document about project and old RFP document and now it can help write based on client requirements and our past RFP My advice? Get involved. Sign up for ChatGPT premium, create your own GPT, or if you're leading a team, develop your own assistants using the API. These digital helpers could become your next competitive edge, much like an diligent interns, ready to streamline your daily tasks and workflows. #AIAssistants #ProductivityTools #Innovation #OpenAI #Teamwork #SJInnovation

Not surprisingly, at Mayfield Fund we are seeing a big wave of Gen AI applications; below are 5 use case themes emerging: 1. Content Generation: LLMs producing custom content for marketing, sales, and customer success, and also create multimedia for television, movies, games, and more. 2. Knowledge CoPilots: Offering on-demand expertise for better decision-making, LLMs act as the frontline for customer questions, aiding in knowledge navigation and synthesizing vast information swiftly. 3. Coding CoPilots: More than just interpretation, LLMs generate, refactor, and translate code. This optimizes tasks such as mainframe migration and comprehensive documentation drafting. 4. Coaching CoPilots: Real-time coaching ensuring decision accuracy, post-activity feedback from past interactions, and continuous actionable insights during tasks. 5. RPA Autopilots: LLM-driven robotic process automation that can automate entire job roles. What else are we missing?

AI for Finance Leaders: TL;DR Edition. AI for Smarter Forecasting: The Power of Sentiment Analysis We all wish we had more time to dive into the research on AI and think about how it will impact us, so today we’re doing just that.  Xiaowei Zhao, Yong Zhou, Xiujuan Xu, Yu Liu recently published a paper called “Extensible Multi-Granularity Fusion Network for Aspect-based Sentiment Analysis” or more simply “Super Smart Tech That Knows What People Really Think”. Let’s break it down to understand what the technology means and how I imagine it could impact the role of finance in the organization. What’s the paper about?   Imagine a super smart tool that reads through thousands of online reviews to find out exactly what people love or don't about a movie, down to the smallest details like the storyline or the graphics. This magic tool is powered by the latest tech called the EMGF (Extensible Multi-Granularity Fusion) network, which is like a detective that can understand feelings and opinions in writing. It's not just about knowing if people liked the movie, but understanding every single part they talked about. Why does it matter for finance? It could be easy to dismiss this technology in finance, but this gives us the ability to analyze written subjective content in mass to create more consistent and accurate forecasts. Here are a few ways that I could see finance tools using this tech to improve your workflow. Practical applications: CRM & Call recording Insights  Imagine using this technology to read through every recorded call with a prospect or customer. Often in sales forecasting, reps use their gut to determine whether they think a deal will move to the next stage. Once you have more than one rep, the consistency starts to plummet. Some are sandbagging, some are looking at the deal with rose-colored glasses, and the results can be all over the place. If AI were able to read through these conversations, it could consistently use sentiment analysis to apply scoring to sales conversations, potentially leading to more accurate and consistent forecasting. Practical applications: Decoding Market Trends We’re also often trying to pull market signals into our forecasts. The problem is there’s so much external data that’s difficult to distill into any kind of useful signal. With the power of EMGF network analysis, we may start to see AI dive into a sea of data from social media, news, etc. to spot the small and large trends that are relevant to our specific markets. It would be like having a map that shows where consumer interests are heading, allowing you to navigate your business strategy with precision and foresight. This insight could further increase our forecast accuracy. How do you see AI-driven sentiment analysis changing your approach to financial analysis and forecasting? #finance #ai #cfo

The Vital Role of AI in Talent Transformation In today's fast-paced world, organizations face the ongoing challenge of adapting to ever-changing business environments. Unlocking the potential of talent within an organization is crucial for success. Looking ahead, artificial intelligence (AI) will play a critical role in talent transformation, revolutionizing how we attract, develop, and retain skilled individuals. 1. Streamlining Talent Acquisition: AI enhances the talent acquisition process by swiftly analyzing vast amounts of data. Intelligent algorithms identify suitable candidates efficiently, screening resumes, and conducting initial interviews. This reduces time and effort for recruiters. AI-powered chatbots and virtual assistants engage with potential candidates, providing information and answering queries, streamlining the recruitment experience. 2. Data-Driven Decision Making: AI-driven analytics enable informed talent management decisions. By analyzing data on employee performance, engagement, and career development, AI identifies patterns, trends, and areas for improvement. These insights optimize talent strategies, personalize learning programs, and identify high-potential individuals for leadership roles. Data-driven decision making ensures effective resource allocation, enhancing employee engagement and retention. 3. Personalized Learning and Development: By leveraging data on skills, performance, and learning preferences, AI recommends relevant courses, resources, and learning pathways. Adaptive learning systems adjust content and pacing to match employees' styles, optimizing skill development. On-demand learning modules powered by AI ensure continuous professional growth, keeping employees relevant in a rapidly evolving job market. 4. Enhancing Employee Experience and Engagement: AI automates mundane tasks and provides real-time support, contributing to a positive employee experience. Virtual assistants manage schedules, answer common queries, and facilitate internal communication, allowing employees to focus on strategic tasks. Sentiment analysis and natural language processing gauge employee feedback, enabling proactive measures to address concerns and enhance engagement. 5. Talent Retention and Succession Planning: By analyzing employee data and aspirations, AI identifies flight risks and develops targeted retention strategies. Predictive analytics anticipate skills gaps, allowing organizations to nurture and upskill existing employees. AI-powered talent marketplaces match internal talent with opportunities, promoting internal growth and reducing turnover. Conclusion: AI is set to become an indispensable part of talent transformation. By optimizing recruitment, enabling data-driven decision making, personalizing learning, enhancing employee engagement, and facilitating retention and succession planning, AI revolutionizes talent management practices.

#artificialintelligence #AI & #RegulatoryChangeManagement #cognitiveGRC #GRC There are a variety of use cases for A.I. in #regulatorychange management. There is not one solution that has all of this covered, so it takes an architecture and often plugs into your favorite enterprise GRC platform for even broader value. These include: Horizon Scanning. Using A.I. to monitor and evaluate pending legislation, proposed rules, changes in enforcement, speeches, and comments made by regulators, and such to determine what we need to pay attention to that will be tomorrow's concerns. Regulatory Obligation Library. Using A.I. to monitor the current situation of regulations, changes in regulations, comparisons of change (side-by-side markups), and notifications, all to keep the organization current with regulatory changes impacting the hear and now. Policy Management. This is the mapping of regulations and changes to your current policy library and leveraging A.I. to inform you what policies should be reviewed because of changes and even perhaps suggest language for the update to address the change (generative A.I.) Control Management. I worked on a very large risk management RFP for a global telecom a few years back. Once they were done with that RFP they then looked to using A.I. to keep controls updated and current in their environment. They specifically leveraged Natural Language Processing to derive content-related information out of local control descriptions. Then used Machine Learning to score quality and identify quality gaps in documentation. This enabled them to provide real-time feedback to control owners directly and indicate areas for improvement. They then did Scoring Reports & Dashboards to generate an overview of the documentation quality of ICS Principles in Business Units And this is just exploring the regulatory change management-related use cases of A.I. I am also seeing a lot of interest in using A.I. for third-party risk management. From reading and comparing differences in policies/controls between an organization and a supplier/vendor, to monitoring the range of third-party risk databases (e.g., ESG ratings, financial viability/corporate ratings, reputation and brand lists, watch lists, sanction lists, negative news, security ratings, politically exposed persons, geo-political risk, and more). It is my job as an analyst to research and understand the variety of GRC solutions (both very narrow and specific to broad platforms) and understand what differentiates one vendor from another and in that context what is the best solution for an organization. In that context, I cover the range of Cognitive GRC solutions available in the market, around the world, and in which industries . . . and know which are real and provide value, and which are ’the Wizard of Oz.’

You have to admit that #artificialintelligence (AI) 🤖 is fascinating. There is much chatter 🗣️ about its uses, ethical standards, and controls. I agree that it is something that requires human 🧍♀️🧍♂️oversight. However, as a medical imaging professional💀, I definitely see the opportunities in the imaging specialty. According to recent data 👨🏿💻, radiology currently has 392 FDA-approved devices, one large area of AI that is taking off 🛫. Why? For starters: the population needing medical imaging is outnumbering the workforce to provide these services. For example, 10,000 Baby Boomers are turning 65 daily; by 2030 (less than seven years), they will all be 65 and older. The ability to capture diagnostic images for this generation promptly and efficiently is a must. Here are some areas that AI is helpful:   🩻 𝑰𝒎𝒂𝒈𝒆 𝑨𝒏𝒂𝒍𝒚𝒔𝒊𝒔: AI algorithms excel at interpreting medical images (i.e., x-ray, CT, MRI, and nuclear medicine), often requiring meticulous attention to detail. Imagine a brain-like computer program that's exemplary at recognizing things in pictures. It helps doctors find problems in medical images by comparing them to pictures it knows well.   🔁 𝑾𝒐𝒓𝒌𝒇𝒍𝒐𝒘 𝑶𝒑𝒕𝒊𝒎𝒊𝒛𝒂𝒕𝒊𝒐𝒏: By automating mundane operations, AI can enhance radiology workflows. This involves selecting and ranking images, highlighting potentially critical cases, and annotating cases for subsequent examination. By lowering the radiologist's burden, AI enables them to devote more time to complex situations, hence enhancing diagnostic accuracy overall.   🤓 𝑸𝒖𝒂𝒏𝒕𝒊𝒕𝒂𝒕𝒊𝒗𝒆 𝑨𝒏𝒂𝒍𝒚𝒔𝒊𝒔: AI can use quantitative methods and image interpretation to measure anatomical structures, volumes, and tissue properties. This helps track disease progression, assess therapy effectiveness, and evaluate postoperative outcomes.   🧐𝑷𝒓𝒆𝒅𝒊𝒄𝒕𝒊𝒗𝒆 𝑴𝒐𝒅𝒆𝒍𝒊𝒏𝒈: Large datasets are analyzed by AI, 𝘢𝘭𝘵𝘩𝘰𝘶𝘨𝘩 𝘯𝘰𝘵 𝘱𝘦𝘳𝘧𝘦𝘤𝘵𝘭𝘺 𝘺𝘦𝘵, to predict patient outcomes and disease development. AI algorithms can aid physicians in making informed judgments regarding treatment plans and therapies by analyzing a variety of criteria, such as patient history, imaging data, and clinical reports.   🩻 𝑰𝒎𝒂𝒈𝒆 𝑬𝒏𝒉𝒂𝒏𝒄𝒆𝒎𝒆𝒏𝒕: One limitation of imaging is quality and noise reduction in obtaining clear diagnostic images. AI is particularly beneficial when patient movement or suboptimal image acquisition might result in blurry images. High-quality images translate to more accurate diagnoses.   The intrigue is undeniable in the realm of Artificial Intelligence 🤖. Undoubtedly, human supervision is crucial 🙏🏻. Yet, from the perspective of medical imaging, I perceive immense prospects within the field 👍🏻, notably the continued increase of its current uses where AI is gaining remarkable traction.

𝐀𝐈 𝐢𝐧 𝐀𝐜𝐭𝐢𝐨𝐧: 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐉𝐨𝐛 𝐑𝐨𝐥𝐞𝐬 𝐚𝐧𝐝 𝐑𝐞𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐞𝐬 Large language models (#LLMs) and other generative AI tools are reshaping the landscape of work as we know it. As these technologies continue to evolve, it's crucial to grasp their implications across various job roles. The recent World Economic Forum report delved into over 19,000 occupational tasks, shedding light on the forthcoming AI disruptions. Here's a glimpse into its key findings: 👉 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 (𝐈𝐓): With 𝟕𝟑% of tasks expected to undergo significant alterations, areas like software quality assurance and customer support are poised for transformation. 👉 𝐅𝐢𝐧𝐚𝐧𝐜𝐞: 𝟕𝟎% of finance tasks are ripe for AI intervention, promising efficiency enhancements in bookkeeping, accounting, and auditing. 👉 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐚𝐥𝐞𝐬: 𝟔𝟕% of tasks in customer sales will be impacted, urging sales professionals to adapt to AI-driven processes. 👉 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: Automation will play a pivotal role in 𝟔𝟓% of operational tasks, spanning supply chain management to logistics. 👉 𝐇𝐮𝐦𝐚𝐧 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 (𝐇𝐑): While automation will touch 𝟓𝟕% of HR tasks, human judgment remains indispensable in certain areas. 👉 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠: 𝟓𝟔% of marketing tasks will undergo changes, optimizing ad targeting and campaign analysis through AI. 👉 𝐋𝐞𝐠𝐚𝐥: AI assistance will impact 𝟒𝟔% of legal tasks, particularly in contract review and legal research. 👉 𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧: With 𝟒𝟑% of tasks experiencing disruption, AI will enhance inventory management and demand forecasting. 🎯 Where to Focus: 𝟏. 𝐈𝐓 𝐚𝐧𝐝 𝐅𝐢𝐧𝐚𝐧𝐜𝐞 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬: Embrace AI tools for efficiency gains and stay ahead of the curve. 𝟐. 𝐔𝐩𝐬𝐤𝐢𝐥𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜𝐚𝐥𝐥𝐲: Identify areas where AI complements human judgment and invest in relevant skills. 𝟑. 𝐒𝐭𝐚𝐲 𝐈𝐧𝐟𝐨𝐫𝐦𝐞𝐝: Keep abreast of AI trends and proactively adapt to new technologies. AI isn't here to replace us—it's here to augment our capabilities! Let's harness its potential to propel us into a future of innovation and growth. 𝐒𝐨𝐮𝐫𝐜𝐞: VisualCapitalist https://lnkd.in/gYCYgn8C #AI #DigitalTransformation #GenerativeAI  #GenAI #Innovation #ArtificialIntelligence #ML #ThoughtLeadership #NiteshRastogiInsights  --------------------------------------------------- • Please 𝐋𝐢𝐤𝐞, 𝐒𝐡𝐚𝐫𝐞, 𝐂𝐨𝐦𝐦𝐞𝐧𝐭, 𝐒𝐚𝐯𝐞 if you find this post insightful • 𝐅𝐨𝐥𝐥𝐨𝐰 me on LinkedIn https://lnkd.in/gcy76JgE to stay connected with my posts.  • Ring the 🔔 for notifications!

Hello, Dear Community! In my ongoing series to showcase real-world applications of AI, I've come across an intriguing project that brilliantly demonstrates how AI and machine learning can elevate business operations and customer interactions. Project Insight: AI Assistant and Bot for Enhanced Scheduling and Engagement The Challenge: Businesses today face the dual challenge of optimizing their scheduling processes and enhancing customer engagement. The key lies in balancing efficient operations with personalized client interactions, a task that becomes increasingly complex as a business grows. The AI Solution: AI Assistant and AI Bot for a scheduling tool, but with a twist – this AI tool is designed to not just schedule but also to engage, analyze, and improve business processes. AI Assistant Features: - Automated Reminders: Sends pre-meeting and post-meeting notifications, ensuring both clients and staff are well-informed. - Trend Analysis: Identifies patterns in meeting times, sales, and client profiles, offering valuable insights for business strategy. - Client Engagement: Collects client information through natural conversations, enhancing the database with valuable insights. - Feedback Collection: Requests customer feedback post-appointment, crucial for service improvement and client satisfaction. - Performance Monitoring: Keeps an eye on team performance and client engagement, providing data for continuous improvement. Stay tuned as we continue to explore innovative AI solutions addressing real-world challenges! #AI #AIBot #MachineLearning #BusinessInnovation