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Introducing Red Corner's Fractional-AI service

The conversation around artificial intelligence, or AI, is omnipresent across sectors; everybody wants AI, and no one knows what AI is, but everyone pretends they do. 

Amidst the widespread enthusiasm about AI's capabilities, it can be challenging to identify practical applications of AI that can enhance your organization's operations and justify the investment.

 Red Corner has developed a structured approach to assist CIOs in transitioning from mere excitement to actionable strategies. The CIO does not need to do this alone; Red Corner’s Fractional-AI executives parachute in, build out their company’s AI capabilities in-house, and see that AI is implemented and rendering value rather than becoming yet another technical leaky bucket.

Phase 1: Construct an AI use case with your Fractional-AI executive

General claims about AI's revolutionary impact (e.g., "AI will revolutionize XYZ ABC," "AI will reshape marketing, ops, revenue and so on") are prevalent. Yet, they often overlook how AI will influence distinct tasks within the business’s value chain. It's beneficial to delineate use cases that AI's role in enhancing your team's responsibilities. 

An AI use case will clearly articulate the following:

  • The specific task AI will assist with (what problem is being addressed?)

  • The action that the AI algorithm will perform to alter the task (how will the problem be solved?)

  • The consequential impact on the task (what changes will this bring to the workflow and team dynamics?)

AI solutions typically target very particular tasks within a department or team. For instance, rather than a broad application like "AI for marketing," AI's roles might include creating content for marketing materials, predicting price changes, and reducing costs through targeted optimizations. 

As you can see, AI is not a black box “do it all” solution but rather a continuous improvement approach that requires an in-house team to build all of the AI needs within the organization; before you know it, every department is asking for AI to help with their operations.

The range of tasks AI can perform is diverse and expanding as AI capabilities evolve. Familiar functions such as sorting, matching, and scoring originate from machine learning models. 

Generative AI introduces new functionalities like enhancing text, image, audio and video, creating content and conversing (with consumers). These novel functions necessitate careful consideration of risks and regulatory requirements. Monitoring the array of AI functionalities adopted is crucial, especially as multimodal AI (integrating multiple AI algorithms) becomes more prevalent.

The impact assessment reconnects the task and AI's role to the broader workflow, indicating potential workflow or team role changes due to AI's intervention. Automation by AI might necessitate alterations in team structure; if processes are streamlined or optimized, this could reduce staff workloads and evolve roles to focus more on overseeing AI outputs.

To exemplify this methodology, consider the following use case.

Identify a task. For example, refining demographic criteria to improve sales outcomes, often undergoes several revisions. Too narrow criteria may lead to inefficient predictions and literal money left on the table. In contrast, overly broad criteria can increase variability and unnecessarily increase marketing and sales costs, rendering the model useless and the project failing.

Your Fractional-AI executive will define the AI's actions within your organization because the AI will be part of it for the next 12-36 months.

Phase 2: Build out your AI capabilities

  • Consult with every department head about the applications of AI and generally get an understanding of the team size and skillset needed

  • Create the AI organization from within, recruiting externally and from within

  • Creating the processes, technical foundation, technical stack and monitoring systems to ensure that it is performing as expected and the thresholds that require retraining

  • Implement “AI Value Attributions” so that we can accurately track the value delivered by AI; this is a whole company initiative that requires the proper systems to be in place.

  • Identify the data requirements and the process to acquire, clean, and transform it.

  • This includes externally acquired data from partners and 3rd party data brokers

  • Work with your various departments to identify how their processes will change and how AI will fit into your new and improved value chain.

  • How human oversight will fit in and the alerts BI needed to oversee AI performance

  • The training and cultural changes needed within your organization to handle your new AI co-workers.

  • Any regulatory compliance that needs to be evaluated and addressed if applicable.

  • The realistic timeframe in which AI needs to generate value

Contact Red Corner today

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