No More Moonshots: IBM Emphasizes Practical AI at Think 2022

W. Stofega

Summary Bullets:

  • CEO Arvind Krishna outlined an AI development strategy that helps businesses become more efficient by eliminating manual processes.
  • IBM’s strategy looks to bring technology to customers to help them accomplish a complete and integrated digital transformation.

Although Think 2022 did not produce headline-grabbing announcements, it did provide customers, the financial community, and the tech press with guidance regarding IBM’s future product strategy. The company’s CEO, Arvind Krishna, expressed intentions to focus on product development related to more practical use cases for artificial intelligence (AI), rather than ‘moonshots.’ To be clear, IBM is not abandoning its heritage of developing new technology; however, Krishna believes that AI projects must bring practical values to the customer and that more ambitious, aspirational initiatives belong in a research lab. One example of IBM’s more pragmatic approach, and the strategy it plans to expand, is how it’s working with McDonald’s. IBM is looking to help the fast-food company use AI to help lower costs and boost efficiency by automating customer orders. An example of a practical solution that will appeal to a broad audience is Watson AIOps, which looks to apply AI to information technology to increase productivity by being predictive, rather than reactive.

The new messaging, which focuses on addressing the immediate needs of a broad customer base, makes sense for IBM.  Many companies are eager to implement AI-driven solutions, but lack the necessary tools and in-house expertise to get started.  According to IBM’s Global AI Adoption Index 2022 (i.e., a survey released in conjunction with Think 2022), only 35% of companies are using AI.  Furthermore, the survey revealed troublingly low adoption of ‘responsible AI’ initiatives. Of those using AI, 74% hadn’t taken steps to reduce bias and 61% hadn’t implemented initiatives related to explaining AI-powered decisions.  A well-rounded AI platform that includes machine learning (ML) and responsbile AI tools can facilitate adoption of these capabilities. IBM stands out as a leader in cloud-based enterprise AI platforms, offering a full suite of AI capabilities and features, industry-specific tools, and complementary offerings (e.g., professional services) to help customers identify and implement use cases. However, to achieve Krishna’s pragmatic approach to AI, IBM will need to make some adjustments. IBM’s AI solution is expensive and best suited for larger companies. To increase its share in AI, IBM will need to review its pricing structure to serve the much more pervasive smaller business customer. Winning with smaller companies will require the elimination of the complexity associated with building ML models. Introducing low-code training designed for companies that do not have the resources to build AI models on their own is a step in the right direction. With its development of the PC, IBM brought computing to businesses both large and small; IBM could benefit by reviewing this earlier success.

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