How IBM, Rillet turn generative AI from a productivity tool into a marketing machine

Upwork CEO Hayden Brown said at Tech Brew’s “Onboarding Your Favorite New Coworker: AI” event this week.

Improved efficiency and productivity is the top outcome business leaders worldwide are trying to drive with AI solutions, per an October 2023 study from Cisco Systems. While CEOs are rightfully focused on how AI can make existing workflows faster, the most forward-thinking leaders are looking at how those same productivity tools can help to generate revenue.

Case study: IBM turns efficiency into insights

IBM helped a telecom company transform generative AI from a documentation tool to a marketing insights generator by solving the problem of inconsistent customer service documentation, said IBM senior partner for global AI and analytics Manish Goyal.

Using AI, the company standardized documentation by converting customer service call transcripts into a common format. After a customer service employee looks over the transcript, it can be fed into a customer relationship management system (CRM) where it becomes a tool for other teams. Marketing and product teams can use generative AI to mine the transcript to see what customer sentiments are, which competitors they’re mentioning, and common complaints about products that can be improved.

That standardization of documentation helped influence marketing strategies and new product development.

Case study: Rillet turns basic conversations into case studies

Enterprise resource planning startup Rillet uses generative AI to turn recordings of conversations with clients into marketing case studies. By inputting question-and-answer session recordings into a generative AI tool, the company can create case studies that are about 70% complete, said founder and CEO Nicolas Kopp.

Keep in mind what data employees are sharing with generative AI. Whether team members are using AI copilots for productivity or more complex tools to create marketing campaigns, “everybody needs to understand what the technology is, how it gets used, how we are using it safely,” said IBM’s Goyal.

“From our perspective, open source is the way to go” when deciding which AI models to use, said Goyal. These models offer “nutrition labels” showing what data the models are trained on. IBM points to TensorFlow, PyTorch, Scikit-learn, and OpenCV as good examples of open-source AI tools.

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