Generative AI ROI: Human Inspired Drivers (Part 1)

In the past few days, one of the most common questions I have been asked by everyone who is wanting to explore and implement GenAI in business is “How do we measure the ROI of Generative AI?” The definition and measurement of “Investment” itself is a bigger question and depends on what role your business wants to play in GenAI – being a producer, aggregator, value adder, or consumer. I will write an article on “how to estimate investments and make a business case for Generative AI” soon but today, let me share some perspectives on “Value drivers for Generative AI ROI”.

Coming from a technology value engineering background, I have quickly realized that it will be a mistake to look for traditional “Business-inspired” value drivers and benefits for Generative AI from the lens of business process improvements alone. As this technology is more “Generative” than “Predictive” or “Deterministic” like other technologies the business has been leveraging for decades, its impact will be more profound and should be measured in domains where humans have always had a monopoly. I propose to use “Human-inspired” value driver tri-facta of “Creativity”, “Innovation”, and “Empathy” for Generative AI in addition to the usual “Business-inspired” value drivers such as “Automation”, “Quality”, “Productivity”, and “Speed to Market”. As businesses consider integrating Generative AI into their operations, it's imperative to adopt a holistic view that encompasses not only the immediate returns but also the long-term strategic benefits. Generative AI is not just a tool for today but a foundation for the future, offering a pathway to sustained success in the digital age.

Creativity

Generative AI stands out as a tool for unparalleled creative generation. By leveraging this technology, businesses can produce unique content, design prototypes, and solve problems in novel ways. The ROI in this context can be quantified by the reduction in time and resources spent on creative processes, alongside the increase in output diversity and quality. Furthermore, Generative AI's ability to inspire human creativity through new ideas and perspectives can indirectly enhance brand value and market differentiation.

• Content Generation Speed: Measure the time taken from ideation to content delivery before and after implementing Generative AI. For instance, the marketing team can reduce the content creation cycle for campaigns from weeks to days, indicating a significant ROI in terms of speed.
• Innovation Quotient: Defined by the number of new ideas or prototypes generated through Generative AI that proceed to testing or development. An example could be a number of presentation or reporting/ visualization ideas, UI/ UX alternatives generated for product development, etc.
• Originality Score: A metric assessing the originality and uniqueness of creative outputs. This will become more pronounced as restrictions and regulations related to copyright are enforced and Generative AI should be used to detect its own creation and qualify it as “original generated” or “inspired generated.”

Innovation

The core of Generative AI's value proposition lies in its potential to drive innovation. This technology can identify patterns and insights from vast datasets beyond human capability, leading to the development of groundbreaking products, services, and processes.

• R&D Cycle Time Reduction: The decrease in time from research and development to product launch. A tech company might use Generative AI to analyze research data, cutting the R&D timeline for their new software solution.
• Market Share Growth: This measure can help to understand how much of the market is captured post-innovation compared to pre-innovation periods. For comparison, when Bing introduced AI enabled search, trends suggested that Bing was gaining some market share for a brief period before Google launched “AI powered” search.
• Patent Filings: The number of new patents filed because of innovations supported by Generative AI. A pharmaceutical firm could track an increase in patent applications for new drugs discovered with the assistance of Generative AI algorithms.

Empathy

As businesses navigate the transformative capabilities of Generative AI, understanding its potential to enhance empathy presents a unique opportunity.

• Customer Sentiment Improvement: Changes in customer sentiment scores pre and post-implementation of Generative AI solutions can indicate enhanced empathetic engagement.
• Employee Engagement Scores: An increase in employee engagement scores following the introduction of Generative AI tools suggests a more empathetic and supportive workplace culture.
• Customer Retention Rates: Higher retention rates may reflect successful empathetic connections with customers, bolstered by personalized and considerate interactions enabled by Generative AI.

Tracking these metrics often requires a combination of internal data analytics platforms, AI performance monitoring tools, and feedback mechanisms to assess qualitative impacts, such as customer satisfaction, employee engagement, and customer perception. As businesses increasingly adopt Generative AI, the development of sophisticated measurement frameworks will become essential for maximizing and demonstrating ROI across these critical dimensions.

I hope the “Human-inspired” Value drivers will provide an alternative approach to business users to identify, estimate, and measure Generative AI impact. But this framework is not complete without the “Business-inspired” value drivers of “Quality”, “Automation”, “Productivity”, and “Speed to Market”. I will cover these in my next article.