According to Scale's latest AI Readiness Report, 78% of companies that have adopted AI have seen enhanced customer experience (CX).
72% of companies now plan to significantly increase their investment in AI each year for the next three years. The three industries planning the highest AI budget increases (insurance, financial services, logistics and supply chain) all have customer experience enhancement as a top 3 priority.
AI is raising the standards for CX
What we're seeing now are large language models (LLMs) used for sentiment analysis (e.g. analyzing customer reviews), email automation (e.g. automated responses to common questions), and customer service bots that actually work to reduce wait times significantly.
But text-only models are limited. Next-gen CX requires a more comprehensive approach. It needs:
- Models that can interpret multiple types of data (text, images, video, audio, and more).
- The ability to rapidly train and update models on multiple types of proprietary data.
What's needed are multimodal models and the infrastructure to leverage them effectively.
Let's look at how a multimodal approach can revolutionize CX in the top three sectors planning to increase their AI budgets. These use cases are all drawn from Scale's respondents.
Insurance
Insurance companies want AI to reduce time, errors, and labor in everyday workflows, which leads to improved customer experience.
Claims processing: insurers must often evaluate images and videos while processing claims. Multimodal models can identify events and damages in these files and summarize them in text, accelerating the claim processing timeline.
Property evaluation: ground and satellite imagery are invaluable in property insurance. Efficiently processing this leads to streamlined assessments for underwriting and risk evaluation.
Financial Services
Financial firms want AI to increase operational efficiency and improve investment decisions.
Investment research: investment analysis deals with data from various sources, including reports, news, social media, podcasts, and more. Multimodal AI can process, interpret, and retrieve all these forms of data, allowing analysts to identify trends and make better-informed investment decisions for their customers.
Fraud detection: Financial fraud often leaves traces in various forms. Processing and detecting patterns across different data sources lead to flagging potential fraudulent activities more accurately.
Logistics and Supply Chain
Logistics companies deal with mountains of paperwork and want AI to modernize their processes.
Demand forecasting: By using AI to analyze data from various sources (sales records, promotional calendars, trends, and even social media sentiment), sectors such as manufacturing and e-commerce can better predict future demand and help maintain optimal inventory levels. This reduces overstock and holding costs, and ensures timely delivery.
Predictive Maintenance: Logistics and supply chain businesses rely heavily on machinery and vehicles. Using AI to interpret data from sensors, logs, and visual inspections can lead to proactive maintenance, preventing downtime, and saving costs.
Multimodal AI is going mainstream
LLMs have dominated the AI landscape for the past several years. In 2023, we're seeing the rise of multimodal models. AI is evolving from working only with text to working with multiple forms of data.
At Kailua Labs, we've built an end-to-end platform that makes the power of multimodal AI accessible. Our content understanding engine understands all your media and helps you build intelligent apps for search, discovery, recommendations, enrichment, and anything else you can imagine.
If you're looking to make AI-enhanced customer experience a reality, let's chat.