Long-term decisions such as strategic investments will require a different set of technology components supporting operational decisions via continuous real-time intelligence. Hence, the need for a wide range of technology components to support DI across both tactical and strategic decisions. The impact of DI will be to advance the use of data and analytics and AI-related components (such as ML) by providing a broad discipline for organizations to use as a framework.
Recommended Actions:
Target specific decisions and build solutions to support those decisions to convince prospects early in the sales cycle of the measurable value that the solutions will offer. Work with the business to identify best application areas based on acceptance to change, use of advanced data techniques, availability of data and desired business outcomes (e.g., managing talent and skill gaps, driving underwriting profitability or enhancing the customer experience in the call center).
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Avoid the “nice to have” trap of providing general technology platforms. Focus on removing costly manual effort and increasing automation in low-value and low-risk decisions as a steppingstone to more advanced decision intelligence. ■
Recommended Reading:
Innovation Insight for Decision Intelligence
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Insurance Scenarios: Sense and Plan for Alternative Futures in an Era of Constant Change ■
When to Automate or Augment Decision Making
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Generative AI Back to Top
Analysis by: Kimberly Harris-Ferrante and Svetlana Sicular
Description: Generative AI refers to AI techniques that learn a representation of artifacts from data, and use it to generate brand-new, unique artifacts that resemble but don’t repeat the original data. These artifacts can serve benign or nefarious purposes. Generative AI can produce totally novel content (including text, images, video, audio, structures), computer code, synthetic data, workflows and models of physical objects. Generative AI also can be used in art, drug discovery or material design.
Gartner, Inc. | G00786204
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