Generative AI in Business Communication

Developing a Human-Centered Innovation Strategy

Slides | Generative AI in Business Communication: Developing a Human-Centered Innovation Strategy

Traditional research and development efforts are expensive and time-consuming, often confined to specialized departments far removed from day-to-day operations. But what if your most valuable AI innovation could come from the people who use it every day? Recent research suggests that frontline workers—given the right tools and autonomy—can become powerful drivers of AI-enabled innovation.

As Wharton School professor, Ethan Mollick notes, “R&D is expensive because it involves lots of trial and error, but when you're doing a task all the time, trial and error is cheap and easy.” This insight takes on new significance in the era of generative AI, where small improvements in prompts or workflows can lead to substantial productivity gains. The key is empowering workers to experiment with AI while maintaining human judgment and oversight throughout the process.

The Promise of AI-Enabled Productivity

Recent studies demonstrate impressive productivity gains from AI adoption across various contexts:

• Customer service representatives showed 14% higher productivity with AI assistance, with novice workers benefiting the most and accelerated learning curves (Brynjolfsson et al., 2023)

• Software developers at major tech companies completed 26.08% more tasks when using AI tools, with less experienced developers showing higher adoption rates and greater gains (Cui et al., 2024)

• Financial analysts using advanced AI prompting techniques outperformed human counterparts in earnings predictions, even without narrative or industry-specific data (Kim et al., 2024)

• Creative tasks saw significant improvements, with AI chatbots producing ideas rated more creative than those from human participants (Bohren et al., 2024)

Perhaps most notably, AI appears particularly effective at helping organizations capture and leverage tacit knowledge—the kind of expertise that's typically difficult to articulate and teach to others. As Brynjolfsson and Unger (2023) note, this capability enables "more workers to spend time working on novel problems, and a growing share of the labor force increasingly comes to resemble a society of research scientists and innovators."

The "Jagged Frontier" of AI Implementation

However, the path to effective AI integration isn't straightforward. Research has identified what some call a "jagged frontier" where AI's usefulness varies dramatically by task:

• While AI excelled at helping analysts develop market plans for new products, it actually hindered performance on complex tasks requiring synthesis of multiple information types (Dell’Acqua et al., 2023)

• Studies in educational settings found that over-reliance on AI for creative tasks led to skill degradation when the tools were removed (Liu et al., 2024)

Building a Human-Centered Innovation Strategy

To maximize benefits while avoiding pitfalls, organizations should focus on empowering their teams and professionals to experiment with AI and sharing their learning. Key elements of this approach include:

1. Empower "Super Users": Enable frontline experts to experiment with AI and develop effective prompts based on their domain knowledge

2. Create Structured Processes: Implement frameworks that combine:

   • Disciplinary expertise

   • Strategic planning

   • Iterative testing

   • Regular evaluation

3. Build Shared Resources: Develop libraries of proven prompts and workflows that can be customized by other team members (Lewis & Dziewulska, 2023)

4. Maintain Feedback Loops: Ensure continuous improvement through regular assessment and refinement of AI-enabled processes

Emerging Practices and Tools

Innovation is happening rapidly in this space, empowering professionals can also help you stay abreast of the latest advancements including these emerging areas of innovation:

• Advanced prompting techniques that help ensure consistent, high-quality outputs

• Custom GPTs and RAG based agents that combine organizational knowledge bases with AI capabilities

• Multi-agent workflows that enable complex task automation while maintaining human oversight

The Path Forward

The key to successful AI integration lies in reflective experimentation. Organizations that empower their teams to experiment with AI while maintaining clear processes for human oversight will be best positioned to:

• Accelerate learning curves for new employees

• Close performance gaps among team members

• Boost overall productivity and innovation

• Maintain quality and human-centered outcomes

The future of work will likely belong not to organizations that simply adopt AI, but to those that develop human-centered approaches that leverage these powerful tools while preserving and enhancing human agency and expertise.

This post is based on a presentation by Abram Anders and Rachel Holmes at the 2024 ABC Conference discussing research and emerging practices in generative AI implementation.