Trends Influencing Data Practices for AI

Data Integration Revolution: ETL, ELT, Reverse ETL, and the AI Paradigm Shift In recents years, we've witnessed a seismic shift in how we handle data integration. Let's break down this evolution and explore where AI is taking us: 1. ETL: The Reliable Workhorse      Extract, Transform, Load - the backbone of data integration for decades. Why it's still relevant: • Critical for complex transformations and data cleansing • Essential for compliance (GDPR, CCPA) - scrubbing sensitive data pre-warehouse • Often the go-to for legacy system integration 2. ELT: The Cloud-Era Innovator Extract, Load, Transform - born from the cloud revolution. Key advantages: • Preserves data granularity - transform only what you need, when you need it • Leverages cheap cloud storage and powerful cloud compute • Enables agile analytics - transform data on-the-fly for various use cases Personal experience: Migrating a financial services data pipeline from ETL to ELT cut processing time by 60% and opened up new analytics possibilities. 3. Reverse ETL: The Insights Activator The missing link in many data strategies. Why it's game-changing: • Operationalizes data insights - pushes warehouse data to front-line tools • Enables data democracy - right data, right place, right time • Closes the analytics loop - from raw data to actionable intelligence Use case: E-commerce company using Reverse ETL to sync customer segments from their data warehouse directly to their marketing platforms, supercharging personalization. 4. AI: The Force Multiplier AI isn't just enhancing these processes; it's redefining them: • Automated data discovery and mapping • Intelligent data quality management and anomaly detection • Self-optimizing data pipelines • Predictive maintenance and capacity planning Emerging trend: AI-driven data fabric architectures that dynamically integrate and manage data across complex environments. The Pragmatic Approach: In reality, most organizations need a mix of these approaches. The key is knowing when to use each: • ETL for sensitive data and complex transformations • ELT for large-scale, cloud-based analytics • Reverse ETL for activating insights in operational systems AI should be seen as an enabler across all these processes, not a replacement. Looking Ahead: The future of data integration lies in seamless, AI-driven orchestration of these techniques, creating a unified data fabric that adapts to business needs in real-time. How are you balancing these approaches in your data stack? What challenges are you facing in adopting AI-driven data integration?

Two weeks ago, while I was off radar on LinkedIn. The concept of data readiness for AI hit me hard… Not just as a trend. But as a gap in how most professionals and organizations are approaching this AI race. I’ve been in this field for over a decade now ▸Working with data. ▸Teaching it. ▸Speaking about it. And what I’ve seen repeatedly is this: We’re moving fast with AI. But our data is not always ready. Most data professionals and organizations focus on: ✓ the AI model ✓ the use case ✓ the outcome But they often overlook the condition of the very thing feeding the system: the data. And when your data isn’t ready → AI doesn’t get smarter. → It gets scarier. → It becomes louder, faster... and wrong. But when we asked the most basic questions, ▸Where’s the data coming from? ▸Is it current? ▸Was it collected fairly? That’s when we show what we are ready for. That’s why I created the R.E.A.D. Framework. A practical way for any data leader or AI team to check their foundation before scaling solutions. The R.E.A.D. Framework: R – Relevance → Is this data aligned with the decision or problem you’re solving? → Or just convenient to use? E – Ethics → Who’s represented in the data and who isn’t? → What harm could result from using it without review? A – Accessibility → Can your teams access it responsibly, across departments and tools? → Or is it stuck in silos? D – Documentation → Do you have clear traceability of how, when, and why the data was collected? → Or is your system one exit away from collapse? AI is only as strong as the data it learns from. If the data is misaligned, outdated, or unchecked, → your output will mirror those flaws at scale. The benefit of getting it right? ✓ Better decisions ✓ Safer systems ✓ Greater trust ✓ Faster (and smarter) innovation So before you deploy your next AI tool, pause and ask: Is our data truly ready or are we hoping the tech will compensate for what we haven’t prepared?

𝐀𝐈 𝐑𝐚𝐜𝐞 𝐇𝐚𝐬 𝐚 𝐍𝐞𝐰 𝐁𝐚𝐭𝐭𝐥𝐞𝐠𝐫𝐨𝐮𝐧𝐝: Databases, Not Just Models. For years, the spotlight in AI has been on models and compute power. But that narrative is Shifting. Today, the competitive edge lies deeper in data infrastructure, memory, and the ability to reason over real-time, high-quality information. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭’𝐬 𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠: 𝟏. 𝐌𝐨𝐝𝐞𝐥𝐬 𝐀𝐥𝐨𝐧𝐞 𝐀𝐫𝐞 𝐍𝐨𝐭 𝐄𝐧𝐨𝐮𝐠𝐡 Even the most advanced LLMs fail when they can’t access clean, timely, and relevant data. Context-rich reasoning depends on the strength of the memory layer behind the model. 𝟐. 𝐓𝐡𝐞 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐥 𝐏𝐫𝐨𝐯𝐞𝐬 𝐈𝐭 • Snowflake acquired Crunchy Data • Databricks acquired Neon for $1 billion • Salesforce acquired Informatica for $8 billion These aren’t random buys. These are strategic moves to own the memory stack for AI agents and applications. 𝟑. 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 𝐀𝐫𝐞 𝐁𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐯𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐬 We’re no longer talking about traditional transactional storage. AI agents need: • Real-time data streams • Semantic retrieval • Fast memory updates • Structured and relational context Databases are now being re-imagined as the Cognitive layer for AI. 𝟒. 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬 The real innovation in AI is happening beneath the surface. Winning enterprises are: • Rethinking their data stack to support AI-native architectures • Combining reasoning engines with real-time memory systems • Moving away from static pipelines to dynamic knowledge access The future of AI is not just about larger models. It's about better Data engines. Memory and Cognition are the next competitive frontiers. If you’re building AI-first systems, don’t just ask “𝐖𝐡𝐢𝐜𝐡 𝐦𝐨𝐝𝐞𝐥 𝐬𝐡𝐨𝐮𝐥𝐝 𝐰𝐞 𝐮𝐬𝐞?” Ask “𝐈𝐬 𝐨𝐮𝐫 𝐝𝐚𝐭𝐚 𝐬𝐭𝐚𝐜𝐤 𝐫𝐞𝐚𝐝𝐲 𝐭𝐨 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐚𝐠𝐞𝐧𝐭-𝐥𝐞𝐯𝐞𝐥 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞?” #AIInfrastructure #Databases #AIAgents #Databricks #Snowflake #Neon

This year, the State of Data and AI Engineering report has been marked by consolidation, innovation and strategic shifts across the data infrastructure landscape. I identified 5 key trends that are defining a data engineering ecosystem that is increasingly AI-driven, performance-focused and strategically realigned. Here's a sneak peek at what the report covers: - The Diminishing MLOps Landscape: As the standalone MLOps space is rapidly consolidating, capabilities are being absorbed into broader platforms, signaling a shift toward unified, end-to-end AI systems. - LLM Accuracy, Monitoring & Performance is Blooming: Following 2024's shift toward LLM accuracy monitoring, ensuring the reliability of generative AI models has moved from "nice-to-have" to business-critical. - AWS Glue and Catalog Vendor Lock-in: While Snowflake just announced read/write support for federated Iceberg REST catalogs, finally loosening its catalog grip, AWS Glue is already offering full read/write federation, and is therefore the neutral catalog of choice for teams avoiding vendor lock-in. - Storage Providers Are Prioritizing Performance: in line with the growing demand for low-latency storage, we see a broader trend in which cloud providers are racing to meet the storage needs of AI and real-time analytics workloads. - BigQuery's Ascent in the Data Warehouse Wars: with 5x the number of customers of both Snowflake and Databricks combined, BigQuery is solidifying its role as a cornerstone of Google Cloud’s data and AI stack. These trends highlight how data engineering is evolving at an unprecedented pace to meet the demands of a rapidly changing technological landscape. Want to dive deeper into these critical insights and understand their implications for your data strategy? Read the full report here: https://lnkd.in/dPCYrgg6 #DataEngineering #AI #DataStrategy #TechTrends #DataInfrastructure #GenerativeAI #DataQuality #MLOps

Since 2012, the Machine Learning, AI & Data (MAD) ecosystem is captured by FirstMark's Landscape reports which show the rapidly evolving ecosystem of AI, data, and analytics. See for an interactive, reader-friendly, and accessible format of the 2024 MAD Landscape: https://mad.firstmark.com/ PDF (below): https://lnkd.in/gwFJfzSe * * * The Landscape's 2024 edition, published in March 2024, now features 2,011 companies, up from 1,416 in 2023 and just 139 in 2012. According to Matt Turck's blog post, providing an overview of the trends, growth is fueled by 2 massive cycles: - The "Data Infrastructure" wave - a decade-long cycle which emphasized data storage, processing, and analytics, from Big Data to the Modern Data Stack. Despite expectations for consolidation in this space, it hasn’t occurred yet, resulting in a large number of companies continuing to operate independently. - The second wave is the "ML/AI cycle", which gained momentum with the rise of Generative AI. Since this cycle is still in its early stages, the MAD Landscape included emerging startups. These 2 waves are deeply interconnected, with the MAD Landscape emphasizing the symbiotic relationship between data infrastructure, analytics/BI and ML/AI, and applications. * * * In the area of AI Governance, Security, and Risk, AI-specific startups and tools are on the rise: - “AI Observability” include startups that help test, evaluate and monitor LLM applications  - “AI Developer Platforms” is close to MLOps, but recognizes the wave of platforms that are wholly focused on AI application development, in particular around LLM training, deployment and inference - “AI Safety & Security” includes companies addressing concerns innate to LLMs, from hallucination to ethics, regulatory compliance, etc * * * 24 key themes shaping the industry are identified: - Distinct pipelines and tools for structured and unstructured data - Maturation and potential consolidation of the Modern Data Stack - Data Quality and Observability: Growing importance of tools that ensure data accuracy and reliability - Increasing focus on data governance frameworks and privacy regulations - Rise of technologies enabling real-time data analytics and decision-making - Data Integration and Interoperability - Data Democratization: Broader access to data and analytics tools - Recognizing the critical contributions of Data Engineers - Impact of Generative AI - Hybrid Future: Coexistence and integration of LLMs and SLMs - Relevance of traditional AI approaches in the era of GenAI - Strategies of orgs building on top of existing AI models vs. developing comprehensive solutions - AI Agents and Edge AI - AI Safety and Ethics - AI Regulation and Policy implications for businesses - Demand for AI Talent and Education - AI in Healthcare - AI in Finance - AI in Retail and E-commerce - AI in Manufacturing - AI in Education - AI in Entertainment and Media - AI and Climate Change - The Future of Work

𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈 𝐈𝐬𝐧’𝐭 𝐀𝐛𝐨𝐮𝐭 𝐁𝐢𝐠𝐠𝐞𝐫 𝐌𝐨𝐝𝐞𝐥𝐬. 𝐈𝐭’𝐬 𝐀𝐛𝐨𝐮𝐭 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐃𝐚𝐭𝐚.  𝐇𝐞𝐫𝐞’𝐬 𝐖𝐡𝐲 𝐃𝐚𝐭𝐚-𝐂𝐞𝐧𝐭𝐫𝐢𝐜 𝐀𝐈 𝐈𝐬 𝐭𝐡𝐞 𝐑𝐞𝐚𝐥 𝐆𝐚𝐦𝐞 𝐂𝐡𝐚𝐧𝐠𝐞𝐫. 1. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:    ↳ Focus on clean, relevant data, not just more data.    ↳ Reduce noise by filtering out irrelevant information.    ↳ Prioritize high-quality labeled data to improve model precision. 2. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:    ↳ Understand the environment your AI operates in. Tailor data accordingly.    ↳ Incorporate real-world scenarios to make AI more adaptable.    ↳ Align data collection with specific business goals for better results. 3. 𝐈𝐭𝐞𝐫𝐚𝐭𝐞 𝐨𝐟𝐭𝐞𝐧:    ↳ Continuously refine data sources to improve model accuracy.    ↳ Implement feedback loops to catch and correct errors quickly.    ↳ Use small, frequent updates to keep your AI models relevant. 4. 𝐁𝐢𝐚𝐬 𝐜𝐡𝐞𝐜𝐤:    ↳ Identify and eliminate biases early. Diverse data leads to fairer AI.    ↳ Regularly audit data for hidden biases.    ↳ Engage diverse teams to broaden perspectives in data selection. 5. 𝐄𝐧𝐠𝐚𝐠𝐞 𝐝𝐨𝐦𝐚𝐢𝐧 𝐞𝐱𝐩𝐞𝐫𝐭𝐬:    ↳ Collaborate with those who understand the data best.    ↳ Leverage expert insights to guide data annotation and validation.    ↳ Involve stakeholders to ensure data aligns with real-world needs. LinkedIn 𝐟𝐨𝐥𝐥𝐨𝐰𝐞𝐫𝐬? Share this post with your network to spark a conversation on why smarter data is the key to AI success. Encourage your connections to think critically about their data strategy. Let's shift the focus from bigger models to better data and make AI truly impactful. Smarter data leads to smarter decisions.  𝐑𝐞𝐚𝐝𝐲 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐚 𝐫𝐞𝐚𝐥 𝐠𝐚𝐦𝐞 𝐜𝐡𝐚𝐧𝐠𝐞𝐫? ♻️ Repost it to your network and follow Timothy Goebel for more. #DataCentricAI #AIInnovation #MachineLearning #ArtificialIntelligence #DataStrategy

Generative AI and data governance, while seemingly opposed, need one another to succeed. That's one of my takeaways after reading Dharma Kuthanur's educational article for Eckerson Group, "Data Governance in the Era of Generative AI." Check out his article here, with excerpts below, and tell us what you think. https://lnkd.in/gRuuvjbQ "Data teams must increasingly focus on fueling accurate and trusted data to LLMs. "GenAI accelerates trends already evident with traditional AI: the importance of data quality and privacy, growing focus on responsible and ethical AI, and the emergence of AI regulations... "To understand the implications, we have to look at this from two angles: 1. "How Data Governance Supports GenAI "As organizations adopt foundational LLMs, their differentiation will come from their own data and knowledge base as inputs to the LLMs. "The growing popularity of fine-tuning and Retrieval Augmented Generation (RAG) for incorporating domain-specific data underscores a few key points: > “'Traditional' data governance (DG) will continue to play a key role in addressing data privacy, security and compliance. > "AI brings a whole new set of challenges such as fairness, transparency and AI ethics, and the need to comply with emerging new AI regulations... > "Unstructured data like text files are the dominant inputs to LLMs. This makes data discovery and classification capabilities for unstructured data a foundational governance requirement. > "As techniques such as RAG see more adoption, the need for real-time DG - for instance, dynamically applying policies to relevant data in an LLM-RAG workflow - will become more important. > "Traditional DG processes provide a well-trodden path for proper management and usage of data across organizations: discover and classify data to identify critical/sensitive data; map the data to policies and other business context; manage data access and security; manage privacy and compliance; and monitor and report on effectiveness. > "Similarly, as DG frameworks expand to support AI governance, they have an important role to play across the GenAI/LLM value chain... 2. "How GenAI Supports Data Governance "GenAI has the potential to turbocharge data democratization and drive dramatic gains in productivity for data teams. [For example, it offers] a natural language interface for data search, and auto-generat[es] business glossary definitions and documentation. "GenAI has the potential to enhance and accelerate many other processes in DG: > "Explain lineage for a report or dataset to enhance trust > "Classify and add metadata tags to unstructured data based on themes/type of content > "Extract regulatory intelligence from policy documents to codify them as technical controls > "Enable dynamic data access control based on policies, roles, permissions and usage context Wayne Eckerson Jay Piscioneri #ai #datagovernance #genai

Data without intelligence is potential; intelligence without action is waste. Databricks' 𝟐𝟎𝟐𝟒 𝐒𝐭𝐚𝐭𝐞 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐀𝐈 𝐑𝐞𝐩𝐨𝐫𝐭 showcases a decisive shift as industries transition from AI experimentation to widespread production, with manufacturing emerging as a standout sector. Companies are leveraging AI to optimize production, enhance quality control, and integrate operational data into decision-making processes. Key takeaways from the report include: • 𝟏𝟏𝐱 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 in machine learning models reaching production, indicating industries are prioritizing real-world AI applications. • 𝟏𝟒𝟖% 𝐲𝐞𝐚𝐫-𝐨𝐯𝐞𝐫-𝐲𝐞𝐚𝐫 𝐠𝐫𝐨𝐰𝐭𝐡 in natural language processing (NLP) use in manufacturing, driving improvements in quality control and customer feedback analysis. • 𝟑𝟕𝟕% 𝐠𝐫𝐨𝐰𝐭𝐡 in vector database adoption, supporting retrieval augmented generation (RAG) to integrate proprietary data for tailored AI applications. • Manufacturing and Automotive lead the charge with a staggering 𝟏𝟒𝟖% 𝐲𝐞𝐚𝐫-𝐨𝐯𝐞𝐫-𝐲𝐞𝐚𝐫 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 in adopting Natural Language Processing (NLP).  Would anyone have picked Manufacturing growing the fastest in NLP?!?! 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐃𝐨 𝐰𝐢𝐭𝐡 𝐓𝐡𝐢𝐬 𝐈𝐧𝐟𝐨? If you’re still debating AI’s value, you’re already late to the game. Manufacturers are moving from “what if” to “what’s next” by putting more AI models into production than ever before — 𝟏𝟏 𝐭𝐢𝐦𝐞𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐥𝐚𝐬𝐭 𝐲𝐞𝐚𝐫!  The most successful organizations are cutting inefficiencies, standardizing processes with tools like data intelligence platforms, and deploying solutions faster. This isn’t just about keeping up with the Joneses; it’s about outpacing them entirely. 𝟏) 𝐈𝐧𝐯𝐞𝐬𝐭 𝐢𝐧 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Use tools like Retrieval Augmented Generation (RAG) and vector databases to turn AI into a competitive advantage by integrating your proprietary data. Don’t rely on off-the-shelf solutions that lack your industry’s nuance. 𝟐) 𝐀𝐝𝐨𝐩𝐭 𝐚 𝐂𝐮𝐥𝐭𝐮𝐫𝐞 𝐨𝐟 𝐒𝐩𝐞𝐞𝐝: The report highlights a 3x efficiency boost in getting models to production. Speed matters — not just for innovation, but for staying ahead of market demands. 𝟑) 𝐄𝐦𝐛𝐫𝐚𝐜𝐞 𝐎𝐩𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞 𝐚𝐧𝐝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧:  The rise of open-source tools means you can innovate faster without vendor lock-in. Build smarter, more cost-effective systems that fit your needs. 𝟒) 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 𝐀𝐈 𝐟𝐨𝐫 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐆𝐚𝐢𝐧𝐬: AI isn’t just for customer-facing solutions. Use it to supercharge processes like real-time equipment monitoring, predictive maintenance, and supply chain resilience. 𝐅𝐮𝐥𝐥 𝐑𝐞𝐩𝐨𝐫𝐭: https://lnkd.in/eZCrq_nF ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

As we step into 2024, I want to extend my warmest wishes to everyone in The Ravit Show Data & AI Community. As we toast to a new beginning, let’s look at 8 Key Trends in Data & AI -- 1. Semantic Layer: This year marks a significant leap in how machines interpret data. We're moving towards a semantic approach where data is not just numbers and text, but meaningful information that machines can understand contextually, and how we interact with AI systems. 2. Data Products: The concept of 'data as a product' is gaining momentum. It’s not just about collecting data anymore; it’s about refining it into a product that delivers real value - turning raw data into a strategic asset for better decision-making and customer insights. 3. Data Platforms: 2024 is seeing the evolution of data platforms into more sophisticated, integrated systems. These platforms are becoming the linchpin of our digital ecosystem, offering seamless access, processing, and analysis of data across various domains. 4. Multimodal Large Language Models (LLMs): LLMs are now going beyond text to understand and interpret multimedia content. This evolution opens up new avenues for AI applications in areas like content creation, media analysis, and interactive entertainment. 5. New Revenue Streams for Cloud Providers in Generative AI: Cloud computing is getting a major boost from generative AI. This symbiosis is creating novel revenue opportunities and transforming how we think about cloud services and AI capabilities. 6. Rise of Prompt Engineering: As AI becomes more prevalent, the art of prompt engineering is becoming critical. It's about effectively communicating with AI to generate precise and relevant outputs, a skill that's rapidly becoming essential in the tech workforce. 7. Data Privacy, Security, and Responsible AI Practices: With great power comes great responsibility. In 2024, there's an intensified focus on ethical AI, prioritizing data privacy and security. It's about building AI systems that are not only powerful but also trustworthy and responsible. 8. Metadata Management: 2024 is witnessing a surge in the importance of metadata in Data & AI. As we deal with ever-increasing volumes of data, managing metadata – the data about data – is becoming crucial. It’s not just about storing and accessing data anymore; it's about understanding its context, quality, and lineage. Effective metadata management leads to better data governance, quality, and usability, making it a pivotal aspect of data strategy in organizations. These trends are not just predictions; they are the pathways leading us to a more innovative and efficient future in Data & AI. What would you like to add? #data #datascience #datapredictions2024 #theravitshow

AI is no longer just an experimentation tool. It’s reshaping the entire optimization landscape. With this shift comes many untapped opportunities. Working with Andrius Jonaitis ⚙️, we've put together a growing list of 40+ AI-driven experimentation tools ( https://lnkd.in/gHm2CbDi) Combing through this list, here are the emerging market trends and opportunities you should know: 1️⃣ SELF-LEARNING, AUTO-OPTIMIZING EXPERIMENTS 💡 Opportunity: AI is creating self-adjusting experiments that optimize in real-time. 🛠️ Tools: Amplitude, Evolv Technology, and Dynamic Yield by Mastercard are pioneering always-on experimentation, where AI adjusts experiences dynamically based on live behavior. 🔮 How to leverage it: Focus on learning and developing tools that shift from static A/B testing to AI-powered, dynamically updating experiments. 2️⃣ AI-GENERATED VARIANTS 💡 Opportunity: AI can help you develop hypotheses and testing strategies. 🛠️ Tools: Ditto and ChatGPT (through custom GPTs) can help you generate robust testing strategies. 🔮 How to leverage it: Use custom GPTs to generate test ideas at scale. Automate hypothesis development, ideation, and test planning. 3️⃣ SMARTER EXPERIMENTATION WITH LESS TRAFFIC 💡 Opportunity: AI-driven traffic-efficient testing that gets results without massive sample sizes. 🛠️ Tools: Intelligems, CustomFit AI, and CRO Benchmark are pioneering AI-driven uplift modeling, finding winners faster -- with less traffic waste. 🔮 How to leverage it: Don't get stuck in a mentality that testing is only for enterprise organizations with tons of traffic. Try tools that let you test more and faster through real-time adaptive insights. 4️⃣ AI-POWERED PERSONALIZATION 💡 Opportunity: AI is creating a whole new set of experiences where every visitor will see the best-performing variant for them. 🛠️ Tools: Lift AI, Bind AI, and Coveo are some of the leaders using real-time behavioral signals to personalize experiences dynamically. 🔮 How to leverage it: Experiment with tools that match users with high-converting content. These tools are likely to develop and get even more powerful moving forward. 5️⃣ AI EXPERIMENTATION AGENTS 💡 Opportunity: AI-driven autonomous agents that can run, monitor, and optimize experiments without human intervention. 🛠️ Tools: Conversion AgentAI and BotDojo are early signals of AI taking over manual experimentation execution. Julius AI and Jurnii LTD AI are moving toward full AI-driven decision-making. 🔮 How to leverage it: Be open-minded about your role in the experimentation process. It's changing! Start experimenting with tools that enable AI-powered execution. 💸 In the future, the biggest winners won’t be the experimenters running the most tests, they’ll be the ones versed enough to let AI do the testing for them. How do you see AI changing your role as en experimenter? Share below: ⬇️