Monday, April 8, 2024

AI Value from "Smarter or Faster" Rather than Virtual?

Some obvious ways artificial intelligence will provide value are by making existing business analytics and decision support software “smarter.”


Online furniture retailer Wayfair used AI to change its lost-sales KPI. “We used to think that if you lost the sale on a particular product, like a sofa, it was a loss to the company,” says CTO Fiona Tan. “But we started looking at the data and realized that 50 percent to 60 percent of the time, when we lost a sale, it was because the customer bought something else in the same product category.”


So now the lost-sales KPI formerly was “item” oriented, it now is “category” oriented. 


Other applications might similarly rely on AI to generate insights the existing structured software packages do not provide, or which humans do not have the time to discover. 

source: BCG, MIT Sloan School of Management 


Analytics Provider

Current Value Proposition

AI Possible Future Value Proposition

Microsoft

Power BI: User-friendly data visualization and reporting. Azure Synapse: Scalable data warehousing and analytics platform.

Power BI: Personalized insights and recommendations through AI-powered data exploration. Azure Synapse: Automated data preparation and anomaly detection for proactive decision-making.

Oracle

Oracle Analytics Cloud: Comprehensive suite for data analysis, reporting, and collaboration.

Oracle Analytics Cloud: AI-driven business simulations and scenario planning for future-proof strategies. Autonomous data management: Self-optimizing databases for improved efficiency and reduced costs.

SAP

SAP Analytics Cloud: Integrated platform for business intelligence, planning, and predictive analytics.

SAP Analytics Cloud: Hyper-personalized dashboards with AI-driven insights tailored to individual user roles. Real-time decision support: AI-powered recommendations and alerts based on dynamic market conditions.

IBM

Watson Analytics: Easy-to-use suite for data exploration, visualization, and predictive modeling.

Watson Analytics: Explainable AI models that provide clear reasoning behind recommendations and predictions. Automated data governance: AI-powered data quality checks and regulatory compliance management.

Tableau (Salesforce)

Tableau: Powerful data visualization tools for interactive dashboards and storytelling.

Tableau: AI-powered data storytelling with automatic chart selection and narrative generation. Conversational analytics: Natural language interaction with data through voice commands or chatbots.

SAS

SAS Viya: Cloud-based platform offering a wide range of analytics capabilities.

SAS Viya: Advanced anomaly detection and root cause analysis using AI and machine learning. Prescriptive analytics: AI-powered recommendations for optimizing business processes and maximizing outcomes.

Qlik

Qlik Sense: Associative analytics platform for fast data exploration and self-service BI.

Qlik Sense: AI-driven anomaly detection and automated insights generation. Augmented data discovery: AI-assisted search and exploration to uncover hidden patterns and relationships within data.

MicroStrategy

MicroStrategy Workstation: Powerful platform for enterprise-grade data analysis and reporting.

MicroStrategy Workstation: AI-powered data preparation and automated data cleansing for improved data quality. Generative AI: Automatic generation of reports and insights based on user queries and preferences.

Domo

Domo Workbench: Cloud-native platform for data integration, visualization, and collaboration.

Domo Workbench: AI-powered data storytelling with automated narrative generation and data-driven recommendations. Predictive forecasting: Continuous forecasting models that adapt to real-time data and market changes.

Sisense

Sisense Fusion Platform: Embedded analytics platform for integrating data insights into applications and workflows.

Sisense Fusion Platform: AI-powered personalization of embedded analytics dashboards based on user behavior and preferences. Automated data monetization: AI-driven identification and recommendation of data-driven revenue opportunities.


No comments: