The Payoff: Turning 2024 Data & AI Investments ROI Positive in 2025
I tuoi presentatori
Thomas Davenport
Data & Analytics Thought Leader & Author
Thomas Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a Fellow of the MIT Initiative for the Digital Economy, and a Senior Advisor to Deloitte Analytics. He has written or edited twenty books and over 250 print or digital articles for Harvard Business Review (HBR), Sloan Management Review, the Financial Times, and many other publications. One of HBR’s most frequently published authors, Thomas has been at the forefront of the Process Innovation, Knowledge Management, and Analytics and Big Data movements. He pioneered the concept of “competing on analytics” with his 2006 Harvard Business Review article and his 2007 book by the same name. Since then, he has continued to provide cutting-edge insights on how companies can use analytics and big data to their advantage, and then on artificial intelligence.
Daniel Hulme
Chief AI Officer at WPP and CEO at Satalia
Daniel Hulme is a globally recognized expert in AI and investor in emerging technologies. He's the CEO of the award winning AI company Satalia, acquired by the advertising company, WPP, where Daniel is also the Chief AI Officer. Daniel is also Co-founder and CEO of the machine consciousness research organization, Conscium. Previously, Daniel was Computer Science Entrepreneur in Residence at UCL. He has been recognized as one of the global top 10 Chief AI Officers, he's a serial TEDx and keynote speaker on AI, ethics, innovation and decentralization, and is a faculty member of SingularityU. Daniel has advisory and executive positions across companies and governments. He holds an international Kauffman Global Entrepreneur Scholarship.
Michelle Bonat
Chief AI Officer at AI Squared
Michelle has 20 years of experience as a tech leader, data scientist, innovator, and evangelist. At AI Squared, she runs a team developing and implementing cutting-edge AI technology to help their customers. Previously, Michelle was CTO of AI at JPMorgan Chase, where she spearheaded AI innovation for 90 products serving 60 million users. She was listed in the Constellation Research AI 150 for her work in AI transformation.
Summary
The discussion investigates the current state of AI investments, with an emphasis on how organizations can achieve ROI from their generative AI initiatives by 2025. The speakers highlight the existing challenges and opportunities in utilizing AI, noting that a significant percentage of AI projects are still in experimental stages with only a few yielding tangible economic benefits. They discuss the varied implications of generative AI across different industries, emphasizing its transformative potential in areas such as content creation and customer operations. The panelists also explore methods for evaluating AI use cases, stressing the importance of prioritization and ethical considerations. The conversation touches on the future of AI, predicting advancements in AI capabilities and their potential impact on industry changes. As Tom Davenport notes, "The challenge is understanding how AI fits into the business model and making sure it's not just a technology in search of a problem."
Key Takeaways:
- Generative AI is still in its early stages, with a focus on experimentation rather than widespread production deployment.
- Ordering AI projects based on impact, risk, and data availability is vital for success.
- AI's potential lies in transforming industries by unlocking new efficiencies and enabling rapid content creation and testing.
- Ethical concerns and explainability are key to implementing AI responsibly.
- Future advancements in AI could lead to more sophisticated, efficient, and widely adopted applications.
Deep Dives
Current State of AI Deployment
The conversation begins with an analysis of the current state of AI deployment, as detailed by Tom Davenport. He notes that "only 5% or 6% of large organizations have any production deployment of generative AI," with the majority still in experimental phases. This experimentation, while valuable for learning, has yet to translate into significant economic returns. The panelists agree that AI has not yet reached its full potential, with Davenport suggesting that the industry is in the "early stages" of realizing ROI from AI investments. This sentiment is echoed by Daniel Houm, who emphasizes the need for organizations to move beyond experimentation to achieve meaningful outcomes.
Industry-Specific Applications
The panelists explore the application of generative AI across various industries, highlighting its transformative potential in sectors like content creation and customer operations. Daniel Houm shares insights from WPP, where AI is used to create content and test audience reactions rapidly, thus enhancing marketing efficiency. Houm describes generative AI as unlocking two critical parts of the marketing supply chain: content creation and testing. However, he also notes the challenge of measuring the quality and value of AI-generated content, a sentiment shared by other panelists who stress the importance of finding the right algorithms to optimize different supply chain aspects. Michelle Bonnet adds that in financial services, AI helps improve internal processes, enhancing efficiency and reducing time to resolution for customer queries.
Frameworks for Evaluating AI Use Cases
A significant portion of the discussion is dedicated to methods for evaluating AI use cases, highlighting the importance of prioritization. Michelle Bonnet suggests a structured approach, recommending that organizations assess potential projects based on impact, risk, and data availability. She advises focusing on high-impact projects, considering risk factors, and ensuring data readiness. This approach, she argues, helps organizations make informed decisions about where to allocate resources and how to maximize the ROI of AI initiatives. The panelists agree that ethical considerations should also be integrated into this method, with Daniel Houm emphasizing the need for explainable AI that is auditable and transparent.
Future of AI and Industry Impact
Looking ahead, the panelists discuss the future trajectory of AI technologies and their potential impact on various industries. Daniel Houm envisions a rapid evolution in AI capabilities, predicting that within 18 months, AI could achieve a level of reasoning akin to a PhD, eventually leading to Artificial General Intelligence (AGI). He cautions, however, that the speed of industry adoption might lag behind technological advancements. Michelle Bonnet highlights the potential for smaller, more efficient AI models and innovations in active inference to drive ROI. The panelists agree that staying informed and strategically using emerging technologies will be vital for organizations seeking to maintain a competitive edge in an AI-driven environment.
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