Chris Blackwell’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

#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  

Application, Data and AI is a practice I need to figure more about as it feels like a well kept secret 🙂 But if the man says, we trust him.. we need to talk and show more about this practice and our proven use cases !!