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Everyone talks about AI. In fact, we also use #AI extensively in #VTDocs. But before delving into the nuances of AI and related technologies, it's important to understand the business context and the challenges it addresses. Let’s consider an example: When reviewing multiple documents, business analysts, project managers, and other professionals typically examine them for essential requirements: They identify dependencies within a document—perhaps paragraph three relates to paragraph 52 on page 19.  These interdependencies occur both within individual documents and across multiple ones.  Examples include standard operating procedures with specific policies, terms and conditions, or supplier agreements.  How does their process look like today? The analyst opens a document and skims through it from start to finish, noting any critical points or elements of interest.  They use the search function (Ctrl + F) to scan for terms like "warranty" or "indemnifications" in the contract. This method is time-consuming and ad-hoc. While experienced professionals might navigate quickly, those less experienced or new to the business need much more time and might overlook crucial details. VT Docs and its AI models offer a different approach: When uploading multiple documents into VT Docs, users instantly see common themes across documents – powered by AI.  Imagine a matrix where each column represents a document, and on the left, thematic frequencies are highlighted.  This visualization allows users to spot patterns quickly. For instance, if references to "indemnification" or variations of that appear across several documents, then VT Docs shows them automatically and provides an aggregated view. VT Docs utilizes an AI model specially designed for business and technical communications.  So, there's no "hallucination" or inaccuracy— the AI doesn't use a generative model (like ChatGPT) but relies on a model that can trace back to the precise text, ensuring 100% accuracy. This precision is non-negotiable and pivotal. Another consideration, especially concerning generative AI, is security and data integrity.  VT Docs and its AI model run completely isolated in the most secure locked-down environments. This ensures no data leaks or unauthorized learning from your data. AI has the potential to transform industries, but only if it maintains reliability and security and seamlessly integrates into existing workflows. At @VisibleThread, we aim to pioneer solutions that embody these principles, ensuring not just innovation but secure and dependable results. #documentreview #artificialintelligence #enterprise

Everyone talks about AI. In fact, we also use #AI extensively in #VTDocs. But before delving into the nuances of AI and related technologies, it's important to understand the business context and the challenges it addresses. Let’s consider an example: When reviewing multiple documents, business analysts, project managers, and other professionals typically examine them for essential requirements: They identify dependencies within a document—perhaps paragraph three relates to paragraph 52 on page 19.  These interdependencies occur both within individual documents and across multiple ones.  Examples include standard operating procedures with specific policies, terms and conditions, or supplier agreements.  How does their process look like today? The analyst opens a document and skims through it from start to finish, noting any critical points or elements of interest.  They use the search function (Ctrl + F) to scan for terms like "warranty" or "indemnifications" in the contract. This method is time-consuming and ad-hoc. While experienced professionals might navigate quickly, those less experienced or new to the business need much more time and might overlook crucial details. VT Docs and its AI models offer a different approach: When uploading multiple documents into VT Docs, users instantly see common themes across documents – powered by AI.  Imagine a matrix where each column represents a document, and on the left, thematic frequencies are highlighted.  This visualization allows users to spot patterns quickly. For instance, if references to "indemnification" or variations of that appear across several documents, then VT Docs shows them automatically and provides an aggregated view. VT Docs utilizes an AI model specially designed for business and technical communications.  So, there's no "hallucination" or inaccuracy— the AI doesn't use a generative model (like ChatGPT) but relies on a model that can trace back to the precise text, ensuring 100% accuracy. This precision is non-negotiable and pivotal. Another consideration, especially concerning generative AI, is security and data integrity.  VT Docs and its AI model run completely isolated in the most secure locked-down environments. This ensures no data leaks or unauthorized learning from your data. AI has the potential to transform industries, but only if it maintains reliability and security and seamlessly integrates into existing workflows. At @VisibleThread, we aim to pioneer solutions that embody these principles, ensuring not just innovation but secure and dependable results. #documentreview #artificialintelligence #enterprise

Everyone talks about AI. In fact, we also use #AI extensively in #VTDocs. But before delving into the nuances of AI and related technologies, it's important to understand the business context and the challenges it addresses. Let’s consider an example: When reviewing multiple documents, business analysts, project managers, and other professionals typically examine them for essential requirements: They identify dependencies within a document—perhaps paragraph three relates to paragraph 52 on page 19.  These interdependencies occur both within individual documents and across multiple ones.  Examples include standard operating procedures with specific policies, terms and conditions, or supplier agreements.  How does their process look like today? The analyst opens a document and skims through it from start to finish, noting any critical points or elements of interest.  They use the search function (Ctrl + F) to scan for terms like "warranty" or "indemnifications" in the contract. This method is time-consuming and ad-hoc. While experienced professionals might navigate quickly, those less experienced or new to the business need much more time and might overlook crucial details. VT Docs and its AI models offer a different approach: When uploading multiple documents into VT Docs, users instantly see common themes across documents – powered by AI.  Imagine a matrix where each column represents a document, and on the left, thematic frequencies are highlighted.  This visualization allows users to spot patterns quickly. For instance, if references to "indemnification" or variations of that appear across several documents, then VT Docs shows them automatically and provides an aggregated view. VT Docs utilizes an AI model specially designed for business and technical communications.  So, there's no "hallucination" or inaccuracy— the AI doesn't use a generative model (like ChatGPT) but relies on a model that can trace back to the precise text, ensuring 100% accuracy. This precision is non-negotiable and pivotal. Another consideration, especially concerning generative AI, is security and data integrity.  VT Docs and its AI model run completely isolated in the most secure locked-down environments. This ensures no data leaks or unauthorized learning from your data. AI has the potential to transform industries, but only if it maintains reliability and security and seamlessly integrates into existing workflows. At @VisibleThread, we aim to pioneer solutions that embody these principles, ensuring not just innovation but secure and dependable results. #documentreview #artificialintelligence #enterprise

Slow Adoption of Generative AI in Business The Wall Street Journal published an article last week titled "Generative AI Isn't Ubiquitous in the Business World—at Least Not Yet" (https://lnkd.in/gTbRiVTk). This article sparked my curiosity about the reasons behind the slow adoption of generative AI (Gen AI) in businesses.  After talking to a cross-section of customers over the past year, I've identified several key factors hindering widespread adoption:   Cost of AI: While consumer-facing AI tools may seem cheap or even free, they can be expensive for organizations. Take ChatGPT4, for example, which charges $10 per 1 million input tokens and $30 per 1 million output tokens. (Think of tokens as pieces of words; 1,000 tokens are roughly equivalent to 750 words.) For even a simple use case, this can translate to tens of thousands of dollars per month.   Lack of Talent: Implementing Gen AI effectively requires a range of skilled and trained professionals. Expertise is needed in Gen AI architecture, implementation, prompt engineering, content moderation, and governance.   Privacy and Security Concerns: Companies worry about their organizational information being fed into AI models. They also have concerns about employees inadvertently sending sensitive data outside firewalls through poorly defined prompts.   Accuracy and Hallucinations: Customers have encountered (and heard stories of) comical and sometimes alarming instances of AI generating nonsensical or misleading responses. This hesitancy to implement customer-facing applications without robust governance is understandable. Co-piloting with human intervention can further increase costs and decrease ROI (return on investment).  Solution:  Successful Gen AI implementations require an upfront AI strategy that addresses these concerns. This strategy should ensure sustainable economic value driven by responsible and ethical AI practices. Prevsiant has developed an AI Strategy approach that can help organizations achieve successful and cost-effective Gen AI implementations. You can review it here: https://lnkd.in/gcGN28sg

If you don't embrace AI you risk losing your competitive advantage but this is a space where when things go wrong, they go really wrong, really fast. If you don't have an effective strategy it will cost you, one way or the other.

Organizations rushing to integrate generative #AI into their technical or sales operations often do so without careful consideration of the state of their organizational data. There are numerous articles (and even warnings within applications like ChatGPT) that say generative AI can be wrong (hallucinations) and to double check important information for accuracy. Now that we know GenAI can be wrong, it's up to an organization to reduce the margin of error so that we can be confident in the responses we get from these GenAI platforms and applications. Why does that matter? At minimum, cost. Machine learning and generative AI require an awful lot of computing power and that comes at a cost, to providers and consumers alike. If it were me, I would prefer if every time we had AI "crunch the numbers" it wasn't an expensive gamble. So, how does an organization make the most effective use of GenAI? It doesn't start with any LLM, or platform, or sick new tool. It starts with #data. Organizations with well-developed data lifecycle and governance policies, coupled with solid foundations in data observability (ensuring data quality, health, timeliness, locality, etc.), will succeed in integrating AI into their organization - because they understand the ol' saying: garbage in, garbage out.

🤖 AI Agents: The Next Big Thing in AI Have you been hearing about AI Agents lately? Everyone should understand them. This latest advancement in AI progression will change the way we process daily tasks by automating complex processes and decision-making. ✨ While generative AI and LLMs can summarise documents and analyse reports etc, they can't access personalised data or take action. For instance, if you ask ChatGPT, "When's my Saturday flight?" – it won't know. That's where AI Agents come in! 🛫 🔮 The Magic of AI Agents: The real magic happens when we integrate AI into our existing processes using the compound method. By putting the AI in charge of the logic – what we call the agent approach – we unlock new possibilities. This is all thanks to significant improvements in LLMs' reasoning capabilities. 🧠💡 We can now feed AI Agents complex problems and watch them devise solutions. They can create plans, execute them, and adjust on the fly – just like a human assistant. 🛠️ How do they work? AI Agents use Retrieval Augmented Generation (RAG) to break down tasks and choose the right components. They have three key capabilities: - Reason: Devise plans, think through each step and adjust their approach - Act: Use external tools like databases, calculators, and APIs - Access Memory: Save and reference past interactions for personalised results ✈️ Let's see it in action: Expanding on the last example, here's how an AI Agent handles it, compared to a standard LLM: - You ask: "Manage my Saturday flight." - It searches your emails for flight details - Queries your personal database for passport info - Uses the airline's API to check you in - Accesses Google Maps to suggest airport routes - Provides a complete travel brief with real-time updates AI Agents are making AI more practical and personalised for everyday use, with the potential to streamline countless business processes. 🏢💡 How do you see AI Agents impacting your industry or daily work? Share your thoughts below! 👇

The future of #GenAI is all about agents… but not this kind of agent! McKinsey & Company produces some of the highest-quality thought leadership content around, and this article by Lareina Yee, Michael Chui, Roger Roberts, and Steve Xu is one of the best I’ve read recently. For the uninitiated, a GenAI-enabled agent is a program that can interact with and adapt to the real world, using a “foundational model” trained on vast, unstructured datasets – rather than relying on rules-based systems. The article puts it far better than I can, but that’s what we’re heading towards: “from thought to action”, as the authors say. For me, one of the key benefits that AI agents bring is what the McKinsey team here call the ability to “manage multiplicity”. That is, to deal with unpredictable workflows, uncertain outcomes, and regular judgment calls. AI is already fundamental to how we see the future of the XLeap platform, starting with AI participating in brainstorming, which will be featured in our next version, v7, due for release at the end of this quarter. We see AI as a key player required to participate in key meetings; a key player that should be present, as AI is increasingly trained in the matters being discussed. However, humans should remain in control and the AI should not be allowed to dominate nor “take over”, nor even automate the brainstorm session completely. Thank you to McKinsey for putting out this kind of practical thought leadership. It really helps those of us working in the AI space get the message across! PS Ironically, this picture was generated by AI. It’s a “suave, international spy as a robot”, because ChatGPT’s content policy prevents it from making images using copyrighted secret agent character James Bond… 😂 #FacilitationSoftware #Consulting #AI https://lnkd.in/eRGZjF4n

The brutal truth about AI in business: Most SMBs are barely scratching the surface. Here's why: They're stuck in the AI implementation trap. So far, I've observed 7 levels of AI adoption: 1. AI Sceptics (Or those who know nothing about AI) 2. ChatGPT Dabblers Tried once, got confused, gave up 3. ChatGPT Co-pilots Regular users, enhancing tasks and writing 4. Custom GPT Creators Reusing GPTs with tailored prompts and knowledge 5. AI Customer Service Champions Chatbots and voice assistants with basic functions 6. AI Automation Wizards Using AI modules to streamline routine tasks 7. AI Agent Orchestrators Deploying specialised AI swarms for complex outputs Here's the kicker: Most businesses are stuck at level 2 or 3. They're leaving massive potential on the table. Want to leapfrog the competition? Start climbing these levels today. Remember: AI isn't just a tool. It's your ticket to exponential growth. Don't get left behind. What level is your business at? Share below, and let's discuss how to level up your AI game. Ready to revolutionise your business with AI? 𝗕𝗼𝗼𝗸 𝗮 𝗳𝗿𝗲𝗲 𝗔𝗜 𝗮𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗰𝗮𝗹𝗹 𝘄𝗶𝘁𝗵 𝗺𝗲: https://lnkd.in/eWDek6yW Let's supercharge your SMB's AI potential together.

Do you know Artificial General intelligence(AGI) the ability of a machine to carry out any intellectual work that a human can? OpenAI has unveiled a five-level framework to monitor its advancement in this direction. Level 1: Chatbots: AI systems are now capable of having conversations with people. The majority of AI in use today, such as ChatGPT from OpenAI and other chatbots like Claude, are in this stage. These systems are helpful for customer service, virtual support, and other applications needing human-like interaction. Level 2: Reasoners: AI systems that can handle issues on a human level are represented by Level 2 "Reasoners". The next milestone is to achieve expert-level reasoning, but current models such as ChatGPT can display reasoning capabilities on occasion. Level 3: Agents :This level of AI allows it to act on behalf of users. This involves carrying out activities, coming to conclusions, and carrying out plans on your own. Agentic AI is being developed by a number of startups, including Devin, the first fully autonomous AI software engineer in history. Level 4: Innovators :The level four AI systems are capable of inventing new things. This includes coming up with unique concepts and inventions in addition to problem-solving techniques. Such AI could revolutionize sectors like science, technology, and engineering by pushing the boundaries of human knowledge and creativity. Level 5: Coordinators :Level 5, where AI can carry out the tasks of large organizations, is the aim of AGI. This degree of AI would be able to oversee intricate procedures, make critical choices, and plan extensive activities. Achieving this level would represent the realization of AGI, where AI surpasses human capabilities in the most economically valuable tasks. According to Bloomberg, OpenAI is close to attaining Level 2. This advancement is critical because it will improve the precision and reliability of AI systems, increasing their utility across a range of industries. OpenAI’s five-level structure provides a clear roadmap for tracking AI progress. It provides a methodical approach to gauging progress and comprehending each step towards artificial intelligence. These stages will aid in directing the creation of ever-more complex systems as AI develops. What do you think about the Open AI 5 Level structure and what you feel can transform the Industry of Tech?