Editorial
AI is no longer a niche interest. It applies to all departments, and organizations are eager to understand the benefits and risks of AI, according to the new research briefing "AI is Everybody's Business" by Barbara Wixom and Cynthia Beath at MIT CISR.
Questions abound: What is AI good for? Can it be trusted? Will it take my job? In response, business leaders are investing in extensive training, partnering with vendors and consultants and collaborating with peers to leverage AI responsibly and at scale.
Their goal is to harness AI's potential while avoiding pitfalls, particularly in data monetization. Organizations that succeed turn data into financial returns by improving work, enhancing products and selling informational solutions. AI empowers their data teams to tackle tasks that are too complex, vast or fast for humans alone. The MIT CISR research highlights how effective leaders use AI to drive remarkable business outcomes, demonstrating that AI, when managed well, can drive significant advancements.
AI is Generating Value
AI is already generating significant value across industries, as illustrated by two notable examples. The Australian Taxation Office (ATO) used machine learning (ML), neural networks, and decision trees to analyze citizen tax-filing behaviors, resulting in respectful nudges that helped citizens comply with work-related expense policies. This initiative led to AUD $113 million in changed claim amounts. CarMax leveraged OpenAI's ChatGPT to aggregate customer reviews and car information from multiple data sets, creating concise and helpful summaries for its online shoppers. This use of AI allowed CarMax to efficiently manage content for an average of 50,000 cars on its website, saving substantial time and resources.
MIT CISR’s research delves into predictive and generative AI from various perspectives, proposing three guiding principles for business leaders making AI investments: 1) build the necessary capabilities for AI, 2) involve everyone in the AI journey and 3) focus on deriving value from AI projects. These principles ensure that organizations not only adopt AI effectively, but also maximize its potential to drive business success.
Principle 1
Investing in practices that build the capabilities required for AI is crucial for success. Organizations need deep data science skills to build and validate models and evaluate their risks, even when using tools and partner solutions with embedded AI models. These skills enable teams to make informed decisions on effectively incorporating AI into their work practices.
However, deep data science skills alone are not enough. Leaders must also invest in complementary skills, ensuring a robust capability in data management, data platforms, acceptable data use and customer understanding. These capabilities are mainly developed through hands-on experience and shaped by new practices, such as training programs, policies, processes and tools. As organizations engage in more sophisticated AI practices, their overall capabilities become more robust, paving the way for successful AI integration.
Principle 2
Involving all stakeholders in your AI journey is essential for success. Engaging a variety of stakeholders helps non-data scientists understand what AI can and cannot do, the timelines for delivering certain functionalities and the costs involved. This broad involvement builds AI literacy across the organization, fostering the development of trustworthy AI models — a crucial capability known as AI explanation.
As highlighted in the upcoming book "The AI-Savvy Leader" by Dave De Cremer, AI-savvy leaders guide their employees in understanding AI. They create a sustainable business model for the future that promotes a collaborative interaction between humans and AI. This inclusive approach ensures that AI initiatives are well-informed and aligned with the organization's goals, clearing the way for more effective and ethical AI integration.
See more: CIOs Share Approaches to Piloting GenAI
Principle 3
Focusing on realizing value from AI projects is paramount, given the high costs associated with AI. Some organizations become sidetracked with endless experimentation, but unless these activities significantly impact how the organization generates revenue or reduces expenses, they are likely a misallocation of resources. Leaders must ensure that AI initiatives are clearly aligned with real challenges and opportunities to achieve tangible financial benefits.
Effective leaders measure and track the outcomes of their AI projects, particularly their financial impacts, and hold someone accountable for achieving the desired financial returns. This disciplined approach ensures that AI investments contribute to the organization's bottom line, providing a clear justification for the costs involved and demonstrating the strategic value of AI initiatives.
Setting Up Success
AI-savvy leaders understand that the question is not whether to incorporate AI, but how to use it most effectively. By adopting a data monetization mindset, they ensure that AI initiatives are aligned with their organization's most critical goals. Quantifying the outcomes of recent AI projects helps demonstrate their value and guide future investments.
To set up for success, leaders should demand transparency from their partners regarding the quality of data used to train AI models and the data practices employed. Active engagement in data monetization initiatives fosters organizational learning and improvement. This creates a virtuous cycle where engagement leads to better data and increased value, sparking new ideas and further engagement. Ultimately, AI is everybody's business, driving continuous improvement and value creation across the organization.
In Conclusion
AI is transforming business practices across industries, necessitating that all organizational stakeholders understand and engage with it. Success with AI requires deep data science skills, broad stakeholder involvement and a focus on tangible value creation, according to "AI is Everybody's Business" by MIT CISR.
Effective AI implementation hinges on building capabilities through hands-on experience, fostering AI literacy and aligning AI projects with strategic business goals. Leaders must ensure transparency in data practices and measure the financial impact of AI initiatives to foster a cycle of continuous improvement and value creation.
See more: Forging the Future: The Fusion of Industry and Innovation
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