Using lessons learned from a decade of applied AI/ML research, we've developed two intelligent digital assistants to enhance the user experience and save valuable time.

As generative AI's capacity to drive tangible business results matures, organizations are looking for concrete ways to integrate this exciting technology into existing operations. One popular use case involves building or augmenting digital assistants with the intelligence of generative AI.

What is a digital assistant?

Digital assistants have been around for a long time (e.g., Clippy, Siri, Alexa, etc.). Modern iterations of these software programs increasingly leverage generative AI to respond to user queries and perform personalized tasks online. This has helped organizations build virtual assistants that deliver more user-friendly interfaces, personalized recommendations, enhanced levels of productivity and connectivity, and streamlined access to a diverse range of data sources across operations.

However, building an AI-powered digital assistant can be challenging given the numerous approaches and tools available in the market. Common barriers to success include:

Lack of access to advanced IT infrastructure and architectures for model development and testing
Immature organizational AI and data delivery capabilities
Prohibitive training costs and timelines
Resistance to adoption from internal users
Sustainability red flags
Outcome uncertainty

Despite these hurdles, Gartner predicts that generative AI will be a workforce partner for 90 percent of organizations by 2025 and that most white-collar employees will use a digital assistant by that time. Nevertheless, we're hearing from clients that it remains unclear exactly how they should approach building or augmenting digital assistants with generative AI.

Quyen Le

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