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Key Trends in GenAI: Be Always in the Know

The latest GenAI trends can take your business by storm, bringing significant transformations, improvements, and competitive advantages powerfully as follows:

1. Hyper-personalized marketing: Marketing That Knows You Better

Imagine a world where every ad you see feels made just for you. Generative AI is making that happen by analyzing tons of data to understand your likes and dislikes. Businesses can now tailor their campaigns so perfectly that it’s almost spooky—in a good way! For example, AI can track your browsing history and suggest exactly the product you didn’t know you needed. It’s like having a personal shopper in your pocket. Plus, in education, platforms use AI to adapt lessons based on how well students do, making learning more effective and fun.

2. AI-driven automation: Getting Stuff Done Automatically

Let’s face it: no one likes spending hours on repetitive tasks. That’s where generative AI comes in. By 2025, it’s expected to automate up to 30% of business operations. Think about supply chain processes, scheduling, or managing employee requests—AI can handle everything. For instance, companies are already using AI to speed up inventory management or even predict what products will be in demand. And customer service? AI-powered bots can answer questions faster and better than ever.

3. Conversational AI: Chatbots That Understand You

If you’ve ever had to deal with a clunky chatbot, you know how frustrating it can be. But conversational AI is leveling up. By 2028, virtual assistants and chatbots will feel more like talking to a helpful human than a robot. They’ll be able to understand complex questions, respond in natural language, and even handle customer complaints like pros. Industries like healthcare and banking are already using this to make their customer service faster and more efficient. No more waiting on hold for hours!

4. Multi-Modal GenAI: AI That Gets All the Details

Generative AI is becoming more intelligent by combining different types of data—text, images, video, and even sound. This is called multi-modal AI, and it’s a big deal. Why? Because it can look at all these inputs together to give a complete picture. For example, retail companies use this technology to analyze how customers behave online and in-store. It helps them precisely recommend what customers want, whether a new outfit or a better grocery deal.

5. AI & Healthcare: A Big Boost for Healthcare

Generative AI is saving lives. Seriously. It’s helping doctors create personalized treatment plans by analyzing patient data, medical history, and test results. AI is also speeding up research, making discovering new drugs and treatments easier. More than 75% of healthcare companies are exploring AI. This means faster diagnoses, fewer errors, and more time for doctors to focus on their patients.

6. AI and Cybersecurity: Fighting Cybercrime Like a Pro

With the rise of digital threats, cybersecurity has become a top priority. Generative AI is stepping in to help by detecting suspicious activity before it becomes a full-blown attack. By 2027, AI-powered systems will drastically reduce false alarms, making it easier for companies to stay safe.

7. Ethics and Regulation: Keeping AI Ethical

As AI gets smarter, questions about how to use it responsibly are growing louder. Governments and companies are working to create rules that ensure AI is fair, safe, and doesn’t harm anyone. For example, the EU focuses on making AI ethical and transparent, while the U.S. takes a more flexible approach to encourage innovation. If you’re a business owner, now’s the time to ensure your AI practices are legal and ethical.

8. Decentralized AI: Better Privacy

What if you could use AI without worrying about your data being misused? That’s what decentralized AI aims to do. Using blockchain technology keeps your data private and secure, giving you more control over its use. This is especially important in sensitive industries like healthcare and finance.

9. GenAI and Business: Bringing Big Wins

In the business world, the true potential of generative AI lies in fine-tuning models to meet specific needs. By 2025, enterprises will hold the reins of AI, leveraging it to drive innovation. Success won't come from simply having vast amounts of data. It will hinge on adapting AI models to work seamlessly with that data. Customized multimodal LLM fine-tuning and evaluation datasets customized to specific use cases enable precise model training. This empowers businesses to unlock the full value of their data and maintain a competitive edge with AI.

10. Open-source models: Pushing Boundaries

This year marks a breakthrough for open-source AI models like Meta’s Llama, Mistral, and Google’s Gemma, offering affordability and accessibility that empower developers and small businesses to innovate. Open-source models like "Llama 3" enable companies to integrate AI seamlessly, transforming businesses of all sizes into AI-driven operations. Open-source fuels innovation and adoption, while closed-source models push AI's boundaries—both are vital for progress.

11. Human-in-the-Loop (HITL): Trend

Human-in-the-loop (HITL) is a key trend, emphasizing the integration of human feedback into Generative AI. This ensures that AI models align with ethical standards, cultural nuances, and real-world needs. HITL improves AI accuracy and fosters collaboration between human expertise and AI. By using HITL, organizations can harness the power of AI while maintaining control over outcomes. Our research also highlights how LLMs can generate inaccurate results without proper human oversight.

12. Agentic AI: Latest Addition

Agentic AI just popped up in mid-2024, the hottest topic for 2025. What's so special about it? This new kind of AI can operate independently, making decisions and acting without needing us to guide it at every step. This is quite different from older AI, which only did what we told it to. These AI agents are especially useful in customer service, where they can handle tasks more efficiently, saving time and increasing productivity, or in finance, where they quickly analyze data and offer recommendations.

13. AI in the Creative Industry: Taking the Lead

Generative AI isn’t just about data and numbers—it’s getting creative too! It’s used to design clothes, create digital art, and even compose music. Imagine a fashion brand using AI to develop the next big trend or a filmmaker creating a blockbuster script with AI’s help. It’s opening up endless media, entertainment, and advertising possibilities.

14. AI in Gaming: Immersive than Ever

If you’re a gamer, get ready for some serious fun. Generative AI creates immersive games by creating dynamic characters, realistic environments, and engaging storylines. Imagine playing a game where the plot changes based on your decisions—AI makes that possible.

Wrapping It Up: The AI Revolution Is Here

Generative AI is more than a tool; it’s a game-changer. From personalized shopping experiences to groundbreaking medical discoveries, it’s transforming every part of our lives. The best part? We’re just getting started. As businesses and individuals, now’s the time to embrace this exciting technology. Whether you’re an entrepreneur, a tech enthusiast, or just someone curious about the future, there’s much to look forward to. So, what are you waiting for? The AI revolution isn’t coming; it’s already here. Let’s make the most of it!

TRANSFORM THE WAY YOU AND YOUR ORGANIZATION WORK WITH DATA –SELF SERVICE AUDIT ANALYTICS Increasing complexities of risks and incessant emergence of disruptive technologies are demanding substantial change in internal audit processes. In today’s world of constant disruption, internal audit should evolve into a dynamic and future-oriented function. Businesses need broad-spectrum audit processes that extend beyond reviewing the obvious. Auditors should adopt forward-looking IA approaches, and should be able to provide deeper and valuable insights on strategy, execution, emerging risks, and hidden opportunities. The 2016 Deloitte Global Chief Audit Executive Survey that polled more than 1200 CAEs from 29 countries and a diverse range of industries, reaffirms the growing need to conduct analytics-based auditing. More than a three quarter of the CAEs (79%) recommend the need for digital disruption and innovation to transform internal audit and enhance its value. The survey also cited the increasing relevance of cutting-edge technologies such as artificial intelligence, cognitive computing, and visual analytics. Is skill-gap a concern? More than half of CAEs (57%) who participated in the survey expressed their intense dissatisfaction about inadequate skills and insufficient expertise of audit teams. When left unaddressed, these skill gaps will weaken auditors’ capabilities to deliver on changing stakeholder expectations. Stakeholders expect more forward-looking analysis to uncover risks and hidden opportunities. Or, gone are the days of static audit reports and analysis of sample data. The Deloitte survey also cites risk anticipation (39%) and data analytics (34%) as the two groundbreaking innovations that are most likely to impact internal audit within the next five years. Changing business landscapes, technological advancements, and proliferation of data have brought forth the imminent need to leverage analytics and data visualization to increase the impact, influence, and effectiveness of internal audit Analytics Adoption Challenges Even after 30 years of inception of data analytics, many auditors continue to adopt conventional internal audit methods and lag in technology adoption. Wondering why? Here are some of the reasons: Can technology disruption be a savior? Advancements in technology are fundamentally changing the nature of the audit and improving its effectiveness and relevance. Here are a few game-changing technology solutions that auditors can harness effectively to enhance the way they work with data: 1.Self-service analytics – Smart analytics for all Self-service analytics is no more a buzzword. It’s the new norm. Self-service analytics empowers auditors to access data, perform queries, and create interactive reports that add richness and granularity to the insights derived. Anyone and everyone, with or without technical expertise, can harness the power of self-service analytics to work seamlessly with large data sets of any size or type, and discover savvy insights without having to write codes or learn programming languages. Key advantages of using self-service analytics tools: Explore your data and create ad-hoc reports without IT skills 2.Mobile Analytics – Audit insights on the go Regardless of the size of an organization or availability of data, it takes weeks to prepare and present comprehensive audit reports. The numbers are usually saved offline as large files or copied to multiple slides for boardroom meetings. Mobile data analytics enables a concise and easily accessible digital avatar of audit reports and dynamic dashboards that can be accessed on mobile devices to interact and proactively monitor the business information on the go. 3.Data Visualization When it comes to presenting audit data, reports and findings, one gets a single view of the entire raw data, all at once. This makes it difficult to decipher what is important and what isn’t, the reason why the point of sharing information gets defeated. Data visualization helps get a flexible and reliable way to identify and share pertinent information in a manner that everyone can easily understand. There are many reasons why auditing is ripe for self-service analytics and visualization driven transformation. There is more data to examine within limited time span. Most financial and operational transactions are moving online, and the number of variables, outliers, trends, and patterns to identify and analyze continue to increase each day. Visual analytics is the fastest way to analyze and understand structured or unstructured data of any size, without IT assistance. Visual technologies help speed up and improve decision making with heat maps, bubble charts, and interactive dashboards that are easy for C-suite executive, non-technical business users, and stakeholders to understand. Everything gets better when you can do it yourself, right? Self-service audit analytics and visualization too are not different. Benefits of self-service analytics in Internal Audit

I’ve always been a huge Tableau fan as they never fail to impress me by bringing out new exciting features in every release. Tableau has yet again released a new version after a thorough research based on people’s experience, thoughts, and comments. As a result, Tableau 2020.3 has an exciting feature with respect to Predictive modelling functions, especially useful to advanced analytics geeks. There are so many other new features which everyone will appreciate – such as IN operator for calculations, Search improvements in data pane, Data quality warnings on Tableau Server and many more! Here are my top picks from the list. The IN Operator The much-awaited feature for the SQL lovers. The IN operator is a shorthand replacement for multiple OR functions. By using IN operator, we will be able to create concise calculation fields instead of never-ending lines of ‘OR’. Let’s look into a simple use case by assuming that I am interested to know the sales value of ‘Apple Products’ compared to all other products, then I can use IN operator to with IF statement to group the products to “Apple Product” and “Other Products” IF [Product Name] IN (‘Apple iPhone 5′,’Apple iPhone 5C’,’Apple iPhone 5S’) THEN “Apple Products” ELSE “Other Products” END Predictive Modelling Function Now the predictive genes would be happy to see more features on the prediction module of Tableau. With the added advantages of the functions MODEL_QUANTILE and MODEL_PERCENTILE, now the prediction function goes beyond the existing trendline analysis. Both the functions will help to build a model that understands how your data is distributed around a best-fit line. MODEL_QUANTILE is a table calculation that returns a target value at a specified percentile, based on other predictors MODEL_PERCENTILE is a table calculation that returns the probability of the expected value being less than or equal to the observed mark, based on other predictors Search improvements in Data pane While developing business specific dashboards, we need to create several calculations and its needs to be adjusted or new calculations needs to be created from existing calculations as per the business unit’s requirements. With the newly added Search improvement feature, our lives are made easy by searching or filtering specific filed based on field name, type, or comments. Search improvements in Data pane Write to external databases in Tableau Prep Write to external databases in Tableau Prep The wow moment for prep is here with the introduction of the ability to output to a database. In all previous versions of Prep, we have been able to write to an extract file or csv, and now from the latest 2020.3 introduces the ability to output to a database. Currently supported databases are SQL Server, MySQL, PostgreSQL, Amazon Redshift, Snowflake, Oracle, and Teradata. As shown in the below example, result from the flow can be saved to a data base table from the output step by choosing “Database table” option, specify your server connection (with login credentials) , choose the data base and then the table. And now, Write to database will power the analytics journey by helping to solve the issues related to, data sources, data security and data governance Open or Upload Workbooks On The Web within Tableau Server Sharing your work and exploring insights from others just got easier. You can now open a Tableau workbook or upload it straight to the web without having to use Tableau Desktop. Simply select the workbook (.twb or .twbx) you want to upload and publish directly to your site on Tableau Server or Online. Only users with the appropriate publishing permissions will have the ability to upload content. Open or Upload Workbooks On The Web within Tableau Server Three refresh options are available, they are Earlier prep was used mainly for Set Data Quality Warnings on Tableau Server Data quality warnings make it possible to let users know when a data asset is flagged, whether because the data is under maintenance, it’s stale, or some other reason. With high visibility data quality warnings, not only let users know about the issue but make sure they do not miss it. Are you interested in learning more about Tableau’s new features or the features? Are you a Tableau user and would like to become a Tableau Champion? If the answer to the above questions is a big “YES”, Please contact us at training@beinex.com/ info@beinex.com and we would be happy to schedule a Tableau demo or training for you and your company.