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Charting a path to the data- and AI-driven enterprise of 2030
By Asin Tavakoli et al., | McKinsey & Company | McKinsey Quarterly 2024
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The excitement around generative AI (gen AI) and its massive potential value has energized organizations to rethink their approaches to business itself. Building on McK’s interactive “The data-driven enterprise of 2025,” help executives think through seven essential priorities that reflect the most important shifts that are occurring, what the main complexities are, and where leaders can focus their energy to realize the data-driven enterprise of 2030.
1. Everything, everywhere, all at once. By 2030, many companies will be approaching “data ubiquity.” Data leaders will need to adopt an “everything, everywhere, all at once” mindset to ensure that data across the enterprise can be appropriately shared and used.
2. Unlocking ‘alpha’. The problem with mass adoption of AI is that many organizations are using the same tools or developing similar capabilities, which means they’re not creating much competitive advantage. To unlock “alpha” (a term investors use for obtaining returns above benchmark levels) with gen AI and other technologies, data leaders need to have a clear focus on data strategies that can deliver competitive advantage.
3. Capability pathways: From reacting to scaling. The ease of use of many basic tools and their increasing availability have generated a proliferation of often-disconnected use cases, pilots, and features. To enable the scale required to operate data-driven businesses in 2030, data leaders will need an approach that accelerates how use cases provide impact while solving for scale through an architecture that can support the enterprise. To achieve this, data leaders need to build “capability pathways,” which are clustered technology components that enable capabilities that can be used for multiple use cases.
4. Living in an unstructured world. For decades now, companies have been working with structured data. That’s just 10 percent of the data available, however. Gen AI has opened up the other 90 percent of data, which is unstructured (for example, videos, pictures, chats, emails, and product reviews). This windfall of data can greatly enrich companies’ capabilities, especially when combined or integrated with other data sources. Data leaders will need to invest in building new capabilities such as natural-language processing to help convert the unstructured data so that LLMs can “understand” and use it, as well as in testing and recalibrating LLMs continually as models and corresponding data sources are updated.
5. The ability of companies to achieve their data and AI vision by 2030 will rely substantially on leadership. To get on the right track, companies need to find leaders who are skilled in three major areas: governance and compliance; engineering and architecture; and business value.
6. The talent profiles of organizations will likely look very different in 2030. These require AI leaders to develop a clear view of what new skills are needed.
7. Guardians of digital trust. Risk has become much more of an area of concern with the rise of advanced technologies—most notably AI and gen AI. Governments are moving quickly to roll out new regulations, and companies are evaluating new policies. Three types of risk stand out: new types of attacks, broadening landscape for risk, and new ‘unknowns.’
2 key takeaways from the article
- The excitement around generative AI (gen AI) and its massive potential value has energized organizations to rethink their approaches to business itself. Organizations are looking to seize a range of opportunities, from creating new medicines to enabling intelligent agents that run entire processes to increasing productivity for all workers. A raft of new risks and considerations, of course, go hand in hand with these developments. At the center of it all is data. Without access to good and relevant data, this new world of possibilities and value will remain out of reach.
- Building on McK’s interactive “The data-driven enterprise of 2025,” helps executives think through seven essential priorities that reflect the most important shifts that are occurring: everything, everywhere, all at once; unlocking ‘alpha’ (data strategies that can deliver competitive advantage); capability pathways: from reacting to scaling; living in an unstructured world; leaders with focusing on three major areas: governance and compliance; engineering and architecture; and business value; understand talent profiles; and to understand, new, broader and unknown risks.
Full Article(Copyright lies with the publisher)
Topics: Strategy, Business Model, Technology, Artificial Intelligence, Data, Security, Human Resource Skills