Noopur Julka, Senior Director, UST, pitches three key pointers on implementing generative AI into strategy and business operations.
Across the globe, AI tools such as machine learning and natural language processing are optimizing business functions, driving business value and boosting employee productivity.
According to research, AI could offer saving opportunities of about $1.4 trillion to $2.6 trillion across functions, including customer service, R&D, manufacturing, supply chain and procurement.
AI is not only automating repetitive tasks but also moving beyond these use cases to assist in higher-level, strategic initiatives to help drive broader business value.
Given this, it is now imperative for business leaders to implement generative AI into their strategy and business operations.
Despite these clear benefits, business leaders face several challenges when trying to successfully incorporate generative AI into their strategy, such as a lack of understanding and expertise, data challenges, and difficulties with integrating AI into existing legacy systems.
With projections that AI could automate about half of today’s business activities a decade earlier than previous estimates projected, here are the ways enterprises and leaders can better prepare themselves.
1. Build a Strong Foundation and Gain a Clear Understanding of AI
Firstly, employers and managers must have a clear understanding of generative AI’s strengths and weaknesses and how this aligns with the organization’s strategic objectives to innovate strategically. To date, one of AI’s major hurdles has been the lack of understanding and knowledge among business leaders. Recent research highlighted that nearly 70% of CEOs revealed uncertainty around generative AI and that this made it challenging to adopt and execute an effective AI strategy. Leaders’ lack of understanding of AI is for several reasons, including the rapid advancements of these technologies making it challenging to keep pace with developments, as well as lack of formal education and training.
To address this, companies need to identify where in the business AI could be used to gain a competitive advantage, the cost-effectiveness of different AI tools – looking at the direct costs, as well as value-added – and the compatibility of AI technology with existing IT infrastructure. As is often the case when it comes to new technologies, there have been a number of examples of organizations that have wasted time and money investing in wrong AI strategies, so this step is crucial in saving on capital and maximizing on AI’s potential to transform business models.
Think of this step as laying the foundation for a skyscraper. Without a solid base, even the most advanced building will crumble. Similarly, without a clear understanding of AI, any strategic implementation is likely to fail.
Additionally, business leaders should focus on continuous education and staying updated with AI advancements. This can be achieved through industry conferences, subscribing to AI research publications, and engaging with AI think tanks. Understanding the ethical considerations and potential biases in AI is also crucial for making informed decisions.
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 cross-departmental 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.
