+ THREE INSIGHTS FOR THE WEEK |
1. Data monetization is a growing priority for organizations looking to gain a strategic advantage and improve profitability, revenue growth, and customer experience. The practice involves the use of data assets to create new revenue streams, reduce costs, or enhance products and offerings.
A survey conducted by the MIT Center for Information Systems Research found that top-performing organizations (in terms of profitability, revenue growth, customer experience, and other factors) attributed 11% of their revenues to data monetization. That’s more than five times the 2% reported by bottom-performing organizations.
One way these high-performing organizations succeed is by focusing on three key factors in building a culture conducive to data monetization:
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CEO-level data leadership. An organizational ability to consistently communicate the CEO’s vision for data motivates investment in data resources and data products.
- Data value realization. Organizations need to be able to move benefits created from data products to the organization’s bottom line, resulting in realized financial value.
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Data resource life-cycle measurement. Organizations must track and manage data assets and other data resources across their life cycles, from their development through the recording of financial returns.
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2. “Software is eating the world,” tech investor Marc Andreessen declared in 2011. Some six years later, Nvidia co-founder and CEO Jensen Huang rejoined, “Software is eating the world … but AI is eating software.”
Now, writing in MIT Sloan Management Review, research fellow Michael Schrage and editorial director David Kiron declare that philosophy is eating AI.
“Philosophy increasingly determines how digital technologies reason, predict, create, generate, and innovate,” they write. “The critical enterprise challenge is whether leaders will possess the self-awareness and rigor to use philosophy as a resource for creating value with AI.”
Specifically, generating sustainable business value with AI demands critical thinking about disparate philosophies regarding what AI models should achieve (teleology), what counts as knowledge (epistemology), and how AI represents reality (ontology), the pair argue.
Deliberately imbuing large language models with philosophical perspectives can radically increase their effectiveness, they write. To maximize that return on AI, leaders should keep in mind that:
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- Organizations need an AI strategy for and with philosophy.
- Leaders and developers alike need to align on the philosophies guiding AI development and use.
- Executives must invest in their own critical thinking skills to ensure that philosophy makes their machines smarter and more valuable.
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3. Beginning this year, companies based in the European Union and those that conduct a significant amount of business there must comply with the region’s Corporate Sustainability Reporting Directive, which requires such businesses to publish regular reports on the social and environmental risks they face.
For most companies, tracking internal emissions is relatively straightforward, but reporting on Scope 3 emissions — which are the result of the activities of external entities, such as third-party suppliers — is more challenging, MIT CISR research scientist Ina Sebastian told TechTarget.
“You have to track your emissions, but you also have to combine that with data from your suppliers and customers to get transparency across your value chain,” Sebastian said.
Many enterprises are introducing initiatives to track carbon emissions as part of their overall digital business transformation efforts, she said. “Companies in Europe … are using more and more digital technologies to help them do that, at least with the tracking and the optimizing,” Sebastian said.
While some companies are building their own software, vendors and their partners, including Normative, SAP, and Workiva, are introducing tools to help meet reporting requirements, TechTarget reports.
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A new look at the economics of AI
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In a new paper, MIT Institute Professor Daron Acemoglu predicts that artificial intelligence will have a “nontrivial, but modest” effect on GDP in the next decade.
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Artificial intelligence research is filled with dramatic forecasts — it will grow between $17.1 and $25.6 trillion annually, according to one prediction.
In “The Simple Macroeconomics of AI,” 2024 Nobel laureate Daron Acemoglu offers a more conservative estimate of how AI will affect the U.S. economy.
For his paper, Acemoglu looked at prior scholarship that analyzed which tasks will be exposed to AI and computer vision technologies and concluded that nearly 20% of all tasks in the U.S. labor market could be replaced or augmented by AI. But only about a quarter of those tasks — or 5% economy-wide — could be profitably performed by AI.
Integrating this figure with expectations around productivity, Acemoglu estimates that the total increase in AI-driven productivity over the next 10 years will be roughly 0.7%. That would average to a 1.1% growth in GDP, which Acemoglu characterized as a “nontrivial, but modest effect.”
Acemoglu still sees great potential in the technology but said that the focus should be on providing truly reliable information in specific problem-solving contexts for workers such as electricians, plumbers, nurses, educators, and clerical workers.
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– David Rand, Professor of Management Science and Brain and Cognitive Sciences, MIT Sloan
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