George Paiva’s Post

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

The journey from #AI experimentation to fullscale #business #transformation is both challenging and rewarding. In my latest article for RTInsights, I delve into how organizations can break free from the confines of AI pilots and scale their initiatives to drive meaningful, long-lasting impact. 🔑 Success in AI is not just about adopting the latest technologies; it’s about crafting a strategy that aligns AI efforts with your broader business goals. This requires a clear vision, strong collaboration across teams, and the ability to navigate the complexities of AI deployment with precision. By focusing on these elements, organizations can unlock AI’s full potential, transforming it from a promising experiment into a powerful engine of growth. 💡 📖 Read the article to learn how you can elevate your AI initiatives and achieve sustainable transformation: https://lnkd.in/g7EmyNK3 #AITransformation 🤖 #Consulting 💼 #SustainableGrowth 🌱 Persistent Systems

#ArtificialIntelligence is reshaping the world — but it isn’t a one-size-fits-all solution that can be easily applied to any business or problem. #AI requires a strategic vision, a clear understanding of the business objectives and challenges as well as robust data infrastructure, a skilled and diverse talent pool, a culture of innovation, and a strong governance and ethical framework.    To truly unlock business value, it’s important to find a trusted partner that can leverage its deep domain and enterprise-grade #AI expertise. At @Kyndryl, we implement enterprise-grade #GenerativeAI and can demonstrate how to responsibly scale #GenAI.   Here are 5 areas that we focus on to help our customers successfully adopt and deploy AI: https://lnkd.in/gFEXF8yq   #TheHeartOfProgress