# Enterprise Functions

When I talk to corporate customers, there is often this idea that AI, while powerful, won’t give any company a lasting competitive edge. After all, over the past two years, large-scale LLMs have become a commodity for everyone. I’ve been thinking a lot about how companies can shape a competitive advantage using AI, and a recent article in the Harvard Business Review ([AI Won’t …](/content/competitive-advantage-with-ai/index.html))

Artificial Intelligence (AI) is reshaping the tech landscape, attracting a diverse array of investors eager to capitalize on its potential. In this article, we explore the key players driving investments in AI, from tech giants and venture capital funds to angel investors, and examine the companies they are betting on. We aim to ([The AI Investment Landscape](/content/ai-investment-landscape/index.html))

AI has played a supporting role in software development for years, primarily automating tasks like analytics, error detection, and project cost and duration forecasting. However, the emergence of generative AI has reshaped the software development landscape, driving unprecedented productivity gains. A McKinsey study reveals that developers using generative AI tools can write ([Code Smarter, Not Harder](/content/ai-for-software-development/index.html))

76% of HR leaders believe their organizations will fall behind if they don't adopt AI solutions in the next year or two. With the rapid pace of AI innovation, we can expect AI to have a major impact on the HR role across the entire employee lifecycle. First, AI will lead to new employee expectations about how they interact with HR and HR technologies, from recruiting and ([AI In HR: A Guide For Business And HR Leaders](/content/ai-in-hr-guide/index.html))

Customer support has become increasingly important, with 88% of buyers saying the experience a company provides matters as much as its products or services. About 72% of customers demand immediate service and nearly 70% expect anyone they interact with to have full context. However, this level of customer care is expensive, leading business leaders to look into AI for higher ([The Future of Customer Support](/content/ai-for-customer-support/index.html))

Both marketing and sales are directly responsible for generating revenue. This unique position gives the two functions significantly more power to direct investment into new projects. The need to keep up with competitors in fighting for market share means that both units are, on average, much more willing to try new tools as well, including the latest technology advances ([Beyond Conventional Tactics](/content/ai-redefining-marketing-and-sales/index.html))

If you’ve read my previous post, you already know why I think you should move to Bayesian A/B testing. In this post, I give a short overview over the statistical models behind Bayesian A/B tests, and present the ways we implemented them at Wix.com — where we deal with a massive scale of A/B tests. I wrote some practical examples in Python along this post. You can easily ([How To Do Bayesian A/B Testing At Scale](/content/bayesian-ab-testing-at-scale/index.html))

What is A/B Testing? Almost everyone hated learning statistics (well, maybe except some statisticians). With all those distributions and critical values that we needed to memorize, we just ended up with a headache. You might have swore not to ever touch the subject again; that is, until you had to analyze an A/B test. A/B testing is the “fun” name ([Why You Should Switch To Bayesian A/B Testing](/content/switch-to-bayesian-ab-testing/index.html))

It’s an exciting time to be working on recommender systems. Not only are they more relevant than ever before, with Facebook recently investing in a 12 trillion parameter model and Amazon estimating that 35% of their purchases come from recommendations, but there is a wealth of powerful, cutting edge techniques with code available for anyone to try. So the ([How Can You Tell If Your Recommender System Is Any Good?](/content/recommender-systems-offline-evaluation/index.html))

Every day you are being influenced by machine learning and AI recommendation algorithms. What you consume on social media through Facebook, Twitter, Instagram, the personalization you experience when you search, listen, or watch Google, Spotify, YouTube, what you discover using Airbnb and UberEATS, all of these products are powered by machine learning and AI recommender ([Improving Diversity Through Recommendation Systems In Machine Learning and AI](/content/diversity-through-recommendation-systems/index.html)