TALKING

2 . Prioritize Enhancing Awareness at the Enterprise Level

Next , enterprise-level awareness of AI is pivotal in its successful adoption , given that executives estimate that up to 40 % of their workforce may need to reskill as a result of implementing AI over the next three years . This involves actively educating and informing all stakeholders within an organization about the benefits , implications and strategic importance of adopting AI technologies . It includes providing workshops and seminars on AI concepts , trends , and its potential impact on the industry , as well as offering training and upskilling programs to equip employees with the necessary skills and knowledge to work effectively with AI tools and systems . At UST , we recognize the importance of this , recently announcing that we will train more than 25,000 employees globally on AI and provide opportunities for career advancements . When companies neglect to focus on this , issues arise around misaligned expectations about AI ’ s capabilities and ineffective use of resources – leading to wasted effort and expenditure .

Imagine AI adoption as preparing a football team for a championship game . Every player needs to understand the game plan , their role , and the capabilities of their teammates . Similarly , AI awareness ensures that every employee understands how AI fits into the company ’ s strategy and how they can contribute to its success .

Developing a culture of AI literacy across the enterprise is essential . This includes organizing crossdepartmental workshops to discuss AI ' s impact on different business areas , promoting AI success stories within the company and encouraging a collaborative approach to AI projects where diverse teams contribute their expertise .

3 . Leverage Your Organization ’ s Knowledge and Assets to Unlock Generative AI Capabilities

It is important for organizations to leverage their own knowledge and assets to create effective , secure and competitive AI solutions . In many cases , outsourced AI solutions do not align with an organization ' s long-term vision and may not fully address the unique needs and challenges of the organization , leading to less effective and relevant outcomes . Likewise , relying on external solutions can limit the organization ’ s ability to innovate independently and develop unique AI capabilities .

To tackle this , organizations need to integrate external solutions with internally developed AI capabilities to mitigate risks and maximize benefits . Using proprietary knowledge allows organizations to develop AI solutions that are specifically tailored to their unique needs , challenges , and goals , as well as allows them to leverage unique assets to foster innovation and develop new products , services or processes that are difficult for competitors to replicate at scale .

To effectively utilize an organization ' s knowledge and assets , companies should implement a robust data management strategy that ensures data quality and accessibility . Encouraging departments to document and share their processes and insights can create a rich repository of knowledge that AI systems can leverage . Additionally , fostering innovation labs or AI centers of excellence within the organization can drive experimentation and rapid prototyping of AI solutions tailored to the company ' s unique needs .

Undeniably , fully capitalizing on AI begins with a clear comprehension of a company ’ s business objectives , equipping your team with the necessary tools and using existing assets to complement AI initiatives .

As a result of these moves , organizational capabilities will evolve , with enhanced decision-making , and we will see AI drive innovation by enabling the development of new products , services , and business models based on advanced data analysis and insights . p

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