Peak Performance Observatory Right-Sizing Your AI Initiatives

As discussed, it is essential to start slowly and gradually gather pace with AI initiatives. Taking on too much can result in disastrous results. These recommendations can help enterprises take the proper steps.

Prioritize ROI and Incremental Adoption:

Focus on foundational AI: To solidify the AI foundation, begin with established AI capabilities like Einstein Opportunity Insights and Account Insights.
Gradual expansion: Progress to more complex use cases involving data analysis and pattern recognition once the initial phase is successful.
Data quality is paramount: Ensure data accuracy and relevance to optimize model performance.

Leverage AI for Predictive and Prescriptive Insights:

Ground models with real-world data: Integrate Salesforce data through SOQL queries to enrich prompts and enhance model accuracy.
Combine data sources: Focus on aggregating data from various platforms to gain comprehensive insights for informed decision-making.
Address core business challenges: Prioritize use cases in semantic search, classification, prediction, and decision-making that directly impact business goals. For example, energy usage reports show how to reduce consumption.
Harness generative AI for prevention: Employ generative AI to analyze data patterns and predict potential issues, enabling proactive measures. For example, it can quickly identify patient health records to remedy health problems.

Human-in-the-Loop and Continuous Improvement:

Combine human expertise with AI: Leverage human judgment to refine AI models and ensure optimal outcomes.
Learn from data and feedback: Continuously analyze model performance and customer behavior to drive improvements.

Organizations can implement AI solutions and drive long-term success by adopting a phased approach, focusing on tangible business benefits, and maintaining a human-centered perspective.

Preventing Outages with AI: An Infosys Salesforce Solution

Infosys' Salesforce practice has developed an outage management application that utilizes AI to predict potential outages based on historical data and real-time metrics. This proactive approach helps businesses minimize downtime and improve overall system reliability.