Neal Bastick’s Post

𝗧𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿 𝗼𝗳 𝗙𝗼𝗿𝘁𝘂𝗻𝗲 𝟱𝟬𝟬 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗳𝗹𝗮𝗴𝗴𝗶𝗻𝗴 𝗔𝗜 𝗿𝗶𝘀𝗸𝘀 𝗵𝗮𝘀 𝘀𝗼𝗮𝗿𝗲𝗱 𝟰𝟳𝟯.𝟱% There’s a unique battle organizations are fighting at the moment. Should they continue to fast-track an unproven technology, such as Generative AI, because the competition is, or do they ensure the responsible execution of it and risk being behind? A survey of annual reports from the biggest U.S. corporations is increasingly highlighting artificial intelligence as a risk. According to a report from research firm Arize AI, the majority of Fortune 500 companies cited AI as a risk (56.2%), a 473.5% increase from the prior year. This jump just so happens to coincide with the explosion of awareness and interest in AI after OpenAI's release of ChatGPT in late 2022. Not surprising. But the challenge with Generative AI is that this new capability is rapidly evolving. Faster than organizations can keep up. Leaving the responsibility aspects as second fiddle. Regardless, the question. . . "If our competitors gain an advantage by using such technologies, our ability to compete effectively and our results of operations could be adversely impacted”. . .still seems to be top-of-mind. This fear of falling behind is driving a frenzied race to adopt AI technologies, often without fully understanding the implications. It's a classic case of FOMO at the corporate level, but with potentially far-reaching consequences. The path forward isn't clear-cut, but it's crucial that organizations don't lose sight of the long-term impact in the rush to adopt AI. So what can they do? 1. Innovation teams tasked with experimenting and implementing AI need to be in lockstep with the governance teams. 2. Governance teams need to understand and focus on the most critical risks to allow for faster adoption Traditional technology implementation processes won't be able to keep up. So the two competing forces of innovation and governance need to join forces. Source: https://lnkd.in/eacn7bUU #AI #Innovation #ResponsibleTech #future #generativeAI

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

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

🛡️ AI in the Boardroom: Caution or Competitive Edge? You Don’t Have to Choose Think AI tools like ChatGPT are stuck in the “trough of disillusionment”? While skepticism is natural, here’s the twist: AI doesn’t need to be perfect to add real value. From summarizing reports to prepping for CEO discussions, these tools can help board members focus on strategy—not busywork. Our latest blog explores how to balance privacy concerns with practical advantages, offering five game-changing ways to use ChatGPT for better meeting preparation. It’s not about replacing human expertise—it’s about working smarter, not harder. 👉 Ready to see how AI can make boardroom decisions sharper? Read the full article and share your thoughts on where you see AI adding value! https://lnkd.in/gyE8rHhv #AIinBoardrooms #GenerativeAI #BusinessStrategy #LeadershipTools #BoardLeadership #AIApplications #FutureOfWork #Innovation #StrategicDecisions

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.

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

For those of you looking to get both a broad understanding of AI along and how to effectively experiment and use it at a new level, I highly recommend this book. Co-Intelligence: Living and Working with AI https://a.co/d/8IfXSyp It made my use of #Claude from Anthropic 4x more effective than ChatGPT.

There is no doubt AI is on course to change how we all work. How fast that evolution happens and what its real impact will be are still up in the air but, in our world of data, people seem to be moving out of the hype phase of “AI now” and “AI everywhere” and into the next phase: considered and measured decisions on how AI can bring actual business benefit. We’re seeing more and more organisations starting to set clear plans on what AI can do; whether it’s driving efficiency, delivering better experience for customers or any other strategic business objective. There’s a couple of things we know for sure: 👉 The old adage of ‘in order to [insert business objective], you need to have a solid foundation of data’ applies to AI; in fact more so than many other initiatives 👉 Having an outcome in mind is vital before you start any AI initiative as we’re seeing more and more projects abandoned as there was never really any goal other than ‘let’s try using AI for that’ Our team at Amplifi are architecting and delivering some really impressive work with several of our clients when it comes to data and AI - and they’re all based on outcomes. We've got a great story to tell about the work we’ve been doing with one of our clients over the past 12-18 months which has resulted in the public release of a private Large Language Model (LLM) application that will result in improved data reliability, actionable insights through predictive analytics, and enhanced user interactions. Better yet, the work we’ve done has already directly contributed to their strategic objectives of minimising churn, increasing membership and ensuring the relevance of the organisation’s research and services. I'll put a link to the story in the comments but the overriding message is clear: absolutely look to take advantage of AI but make sure you temper it with good data and outcomes in mind. P.S. No, I didn't use ChatGPT to write this. 😂 #artificialintelligence