Practical AI Use Cases Beyond Fragmented

The Signal

The recent launch of AI marketplaces and platforms for specific industries, such as the energy sector and additive manufacturing, signals a shift towards more practical and specialized AI applications. For instance, SLB's AI marketplace for the energy sector and Synera's agentic AI for additive manufacturing design and build preparation demonstrate the potential for AI to enhance industry-specific workflows.

Why It Matters

This shift matters because it highlights the need for organizations to move beyond fragmented automation and towards workflow-native intelligence. As noted in the article on operationalizing AI in mortgage lending, achieving this requires a more holistic approach to AI implementation, where AI is integrated into the core workflow rather than being used as a peripheral tool. The cost of compute for AI, as mentioned by an Nvidia executive, also underscores the importance of strategic AI adoption to maximize benefits while minimizing expenses.

Where It Gets Practical

Practical applications of AI can be seen in various sectors, including mortgage lending, where AI can help streamline processes and improve decision-making. The concept of agentic AI, which involves using AI to empower humans rather than replace them, is particularly relevant here. By leveraging AI in a way that complements human capabilities, organizations can achieve more efficient and effective workflows. For example, AI can be used to analyze large datasets and provide insights that human professionals can then use to make informed decisions.

The Constraint

One of the constraints to widespread AI adoption is the cost of compute, which can be prohibitively expensive for many organizations. As mentioned, the cost of running AI models can far exceed the costs of human labor, making it essential for companies to carefully evaluate their AI strategies and ensure that they are using AI in a way that provides a significant return on investment. Additionally, the need for specialized AI platforms and marketplaces for different industries can create complexity and fragmentation, making it harder for organizations to navigate the AI landscape.

What I Would Try First

Given these considerations, one approach to try first would be to identify specific, high-impact areas within an organization's workflow where AI can be applied to drive significant improvements. This might involve conducting a thorough analysis of current processes and pinpointing areas where AI can help reduce costs, enhance efficiency, or improve decision-making. By focusing on these key areas and adopting a strategic, workflow-native approach to AI implementation, organizations can begin to realize the practical benefits of AI while managing the associated costs and complexities.

Sources

• Forbes 2026 AI 50 List | Top Artificial Intelligence Companies - Forbes
• ‘The cost of compute is far beyond the costs of the employee’: Nvidia executive says right now AI is more expensive than paying human workers - Fortune
• iAmerica Launches Curated Hub for Practical AI Use Cases - The Tennessean
• Operationalizing AI in Mortgage Lending: Moving beyond fragmented automation to workflow-native intelligence - HousingWire
• SLB launches AI marketplace for energy sector - Splash247
• Synera brings agentic AI to additive manufacturing design and build preparation - VoxelMatters