Arion Research LLC’s Post

💰 𝗧𝗵𝗲 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜: 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗹𝗲𝘀𝘀𝗼𝗻𝘀 𝘁𝗼 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝗰𝗼𝘀𝘁 𝗮𝗻𝗱 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝗺𝗽𝗮𝗰𝘁! Two of the main reasons why AI projects are likely to be shelved are a failure to achieve forecasted results and the difficulty in assessing AI's ROI. The cost of using LLMs must be always evaluated at the initial stages of the project, specially if you don't want to waste your scientist team time. However, assessing this cost may not be straightforward and should go beyond price per token or $ $/GPU/Hour... You really need to carefully select the metrics used to determine ROI, and this is what will define how much you are willing to pay. Some important elements you will need to evaluate sooner than later: ⌛ Time savings 🏋♂️ Productivity increase 💰 Cost savings 📈 Revenue increase 🤗 Better Experience 👨💻 Skills retention 🤸♂️ Agility 👇 The most successful AI adopters use the following tried-and-true strategy to launch AI initiatives, assess progress, and intelligently scale successful use cases: 1️⃣ Identify a Use Case and Solvable Problem 2️⃣ Conduct an AI Feasibility Analysis 3️⃣ Collect and Prepare Data 4️⃣ Deploy the AI Platform 5️⃣ Monitor and Assess the ROI This approach will help customers spend money wisely and clearly articulate the benefits of these technologies to senior leadership and stakeholders. #ai #ROI #genai

Successful implementation of generative AI (GenAI) in organizations requires a comprehensive approach based on six key pillars: Establish AI control tower Reimagining business models Ensuring AI confidence Addressing talent and tech gaps Developing alliance Driving focused data maturity Summary Organizations need an AI control tower to oversee use cases, set priorities, and avoid duplicated efforts across the enterprise Leaders must reimagine future business models and functions instead of merely fitting GenAI into existing processes Continuous testing, governance, and ethical frameworks are essential to ensure confidence in AI systems Companies should address talent gaps through training and consider various approaches (build, buy, or hybrid) to fill technology gaps Developing an ecosystem of alliances with technology partners, academics, professional services, and data partners is crucial for success A focused data maturity strategy is needed to make data AI-ready, emphasizing accessibility, visibility, timeliness, openness, reliability, expansiveness, and trust/security The EY AI Anxiety in Business survey found that 80% of employees would feel more comfortable with AI if trained, but 73% weren't getting needed coaching Organizations must consider how GenAI affects every level of the company and be open to new workforce needs and thinking Responsible AI practices, including fairness, accountability, and reliability, should be integrated into GenAI implementation The EY.ai Confidence Index offers a framework for enhancing decision-making and efficient operations through responsible AI

Successful implementation of generative AI (GenAI) in organizations requires a comprehensive approach based on six key pillars: Establish AI control tower Reimagining business models Ensuring AI confidence Addressing talent and tech gaps Developing alliance Driving focused data maturity Summary Organizations need an AI control tower to oversee use cases, set priorities, and avoid duplicated efforts across the enterprise Leaders must reimagine future business models and functions instead of merely fitting GenAI into existing processes Continuous testing, governance, and ethical frameworks are essential to ensure confidence in AI systems Companies should address talent gaps through training and consider various approaches (build, buy, or hybrid) to fill technology gaps Developing an ecosystem of alliances with technology partners, academics, professional services, and data partners is crucial for success A focused data maturity strategy is needed to make data AI-ready, emphasizing accessibility, visibility, timeliness, openness, reliability, expansiveness, and trust/security The EY AI Anxiety in Business survey found that 80% of employees would feel more comfortable with AI if trained, but 73% weren't getting needed coaching Organizations must consider how GenAI affects every level of the company and be open to new workforce needs and thinking Responsible AI practices, including fairness, accountability, and reliability, should be integrated into GenAI implementation The EY.ai Confidence Index offers a framework for enhancing decision-making and efficient operations through responsible AI

Successful implementation of generative AI (GenAI) in organizations requires a comprehensive approach based on six key pillars: Establish AI control tower Reimagining business models Ensuring AI confidence Addressing talent and tech gaps Developing alliance Driving focused data maturity Summary Organizations need an AI control tower to oversee use cases, set priorities, and avoid duplicated efforts across the enterprise Leaders must reimagine future business models and functions instead of merely fitting GenAI into existing processes Continuous testing, governance, and ethical frameworks are essential to ensure confidence in AI systems Companies should address talent gaps through training and consider various approaches (build, buy, or hybrid) to fill technology gaps Developing an ecosystem of alliances with technology partners, academics, professional services, and data partners is crucial for success A focused data maturity strategy is needed to make data AI-ready, emphasizing accessibility, visibility, timeliness, openness, reliability, expansiveness, and trust/security The EY AI Anxiety in Business survey found that 80% of employees would feel more comfortable with AI if trained, but 73% weren't getting needed coaching Organizations must consider how GenAI affects every level of the company and be open to new workforce needs and thinking Responsible AI practices, including fairness, accountability, and reliability, should be integrated into GenAI implementation The EY.ai Confidence Index offers a framework for enhancing decision-making and efficient operations through responsible AI

Successful implementation of generative AI (GenAI) in organizations requires a comprehensive approach based on six key pillars: Establish AI control tower Reimagining business models Ensuring AI confidence Addressing talent and tech gaps Developing alliance Driving focused data maturity Summary Organizations need an AI control tower to oversee use cases, set priorities, and avoid duplicated efforts across the enterprise Leaders must reimagine future business models and functions instead of merely fitting GenAI into existing processes Continuous testing, governance, and ethical frameworks are essential to ensure confidence in AI systems Companies should address talent gaps through training and consider various approaches (build, buy, or hybrid) to fill technology gaps Developing an ecosystem of alliances with technology partners, academics, professional services, and data partners is crucial for success A focused data maturity strategy is needed to make data AI-ready, emphasizing accessibility, visibility, timeliness, openness, reliability, expansiveness, and trust/security The EY AI Anxiety in Business survey found that 80% of employees would feel more comfortable with AI if trained, but 73% weren't getting needed coaching Organizations must consider how GenAI affects every level of the company and be open to new workforce needs and thinking Responsible AI practices, including fairness, accountability, and reliability, should be integrated into GenAI implementation The EY.ai Confidence Index offers a framework for enhancing decision-making and efficient operations through responsible AI

Successful implementation of generative AI (GenAI) in organizations requires a comprehensive approach based on six key pillars: Establish AI control tower Reimagining business models Ensuring AI confidence Addressing talent and tech gaps Developing alliance Driving focused data maturity Summary Organizations need an AI control tower to oversee use cases, set priorities, and avoid duplicated efforts across the enterprise Leaders must reimagine future business models and functions instead of merely fitting GenAI into existing processes Continuous testing, governance, and ethical frameworks are essential to ensure confidence in AI systems Companies should address talent gaps through training and consider various approaches (build, buy, or hybrid) to fill technology gaps Developing an ecosystem of alliances with technology partners, academics, professional services, and data partners is crucial for success A focused data maturity strategy is needed to make data AI-ready, emphasizing accessibility, visibility, timeliness, openness, reliability, expansiveness, and trust/security The EY AI Anxiety in Business survey found that 80% of employees would feel more comfortable with AI if trained, but 73% weren't getting needed coaching Organizations must consider how GenAI affects every level of the company and be open to new workforce needs and thinking Responsible AI practices, including fairness, accountability, and reliability, should be integrated into GenAI implementation The EY.ai Confidence Index offers a framework for enhancing decision-making and efficient operations through responsible AI

Successful implementation of generative AI (GenAI) in organizations requires a comprehensive approach based on six key pillars: Establish AI control tower Reimagining business models Ensuring AI confidence Addressing talent and tech gaps Developing alliance Driving focused data maturity Summary Organizations need an AI control tower to oversee use cases, set priorities, and avoid duplicated efforts across the enterprise Leaders must reimagine future business models and functions instead of merely fitting GenAI into existing processes Continuous testing, governance, and ethical frameworks are essential to ensure confidence in AI systems Companies should address talent gaps through training and consider various approaches (build, buy, or hybrid) to fill technology gaps Developing an ecosystem of alliances with technology partners, academics, professional services, and data partners is crucial for success A focused data maturity strategy is needed to make data AI-ready, emphasizing accessibility, visibility, timeliness, openness, reliability, expansiveness, and trust/security The EY AI Anxiety in Business survey found that 80% of employees would feel more comfortable with AI if trained, but 73% weren't getting needed coaching Organizations must consider how GenAI affects every level of the company and be open to new workforce needs and thinking Responsible AI practices, including fairness, accountability, and reliability, should be integrated into GenAI implementation The EY.ai Confidence Index offers a framework for enhancing decision-making and efficient operations through responsible AI

Successful implementation of generative AI (GenAI) in organizations requires a comprehensive approach based on six key pillars: Establish AI control tower Reimagining business models Ensuring AI confidence Addressing talent and tech gaps Developing alliance Driving focused data maturity Summary Organizations need an AI control tower to oversee use cases, set priorities, and avoid duplicated efforts across the enterprise Leaders must reimagine future business models and functions instead of merely fitting GenAI into existing processes Continuous testing, governance, and ethical frameworks are essential to ensure confidence in AI systems Companies should address talent gaps through training and consider various approaches (build, buy, or hybrid) to fill technology gaps Developing an ecosystem of alliances with technology partners, academics, professional services, and data partners is crucial for success A focused data maturity strategy is needed to make data AI-ready, emphasizing accessibility, visibility, timeliness, openness, reliability, expansiveness, and trust/security The EY AI Anxiety in Business survey found that 80% of employees would feel more comfortable with AI if trained, but 73% weren't getting needed coaching Organizations must consider how GenAI affects every level of the company and be open to new workforce needs and thinking Responsible AI practices, including fairness, accountability, and reliability, should be integrated into GenAI implementation The EY.ai Confidence Index offers a framework for enhancing decision-making and efficient operations through responsible AI

Successful implementation of generative AI (GenAI) in organizations requires a comprehensive approach based on six key pillars: Establish AI control tower Reimagining business models Ensuring AI confidence Addressing talent and tech gaps Developing alliance Driving focused data maturity Summary Organizations need an AI control tower to oversee use cases, set priorities, and avoid duplicated efforts across the enterprise Leaders must reimagine future business models and functions instead of merely fitting GenAI into existing processes Continuous testing, governance, and ethical frameworks are essential to ensure confidence in AI systems Companies should address talent gaps through training and consider various approaches (build, buy, or hybrid) to fill technology gaps Developing an ecosystem of alliances with technology partners, academics, professional services, and data partners is crucial for success A focused data maturity strategy is needed to make data AI-ready, emphasizing accessibility, visibility, timeliness, openness, reliability, expansiveness, and trust/security The EY AI Anxiety in Business survey found that 80% of employees would feel more comfortable with AI if trained, but 73% weren't getting needed coaching Organizations must consider how GenAI affects every level of the company and be open to new workforce needs and thinking Responsible AI practices, including fairness, accountability, and reliability, should be integrated into GenAI implementation The EY.ai Confidence Index offers a framework for enhancing decision-making and efficient operations through responsible AI

Successful implementation of generative AI (GenAI) in organizations requires a comprehensive approach based on six key pillars: Establish AI control tower Reimagining business models Ensuring AI confidence Addressing talent and tech gaps Developing alliance Driving focused data maturity Summary Organizations need an AI control tower to oversee use cases, set priorities, and avoid duplicated efforts across the enterprise Leaders must reimagine future business models and functions instead of merely fitting GenAI into existing processes Continuous testing, governance, and ethical frameworks are essential to ensure confidence in AI systems Companies should address talent gaps through training and consider various approaches (build, buy, or hybrid) to fill technology gaps Developing an ecosystem of alliances with technology partners, academics, professional services, and data partners is crucial for success A focused data maturity strategy is needed to make data AI-ready, emphasizing accessibility, visibility, timeliness, openness, reliability, expansiveness, and trust/security The EY AI Anxiety in Business survey found that 80% of employees would feel more comfortable with AI if trained, but 73% weren't getting needed coaching Organizations must consider how GenAI affects every level of the company and be open to new workforce needs and thinking Responsible AI practices, including fairness, accountability, and reliability, should be integrated into GenAI implementation The EY.ai Confidence Index offers a framework for enhancing decision-making and efficient operations through responsible AI