Is generative AI the chicken or the egg? Generative AI is being called upon to “save the day” for businesses, with warnings that those slow to adopt may be left behind. Shareholder pressure on leaders to leverage generative AI for operational efficiency and faster market entry has intensified. Yet, the vast potential of AI can feel overwhelming, leading some businesses to delay adoption.
We propose a different approach to adopting generative AI or any technology-driven solution. Don't begin by investing in generative AI and then search for a use case after the fact. Instead, identify a specific business challenge and use generative AI to prototype one or more possible solutions. This challenge-first approach makes experimenting with new technologies more realistic and appealing to stakeholders.
No Risk, No Reward
The conditions of a tight economy and weaker confidence inevitably create an environment of “short-term” thinking. In this state, business leaders are more conservative and risk averse, with immediate attention on short-term quarterly earnings than on long-term strategy. While the “play it safe” mindset might seem prudent in the short term, it can also mean opportunity cost in the long term. The world of business, overall, then becomes less competitive and less innovative.
Many business leaders and their teams also show reluctance toward embracing AI tools, with fears around the quality of the outputs, combined with the fear of job displacements. And no one wants to take the risk of being a first-mover using something that's new and untested.
We all need to remember that there is no such thing as a zero-risk business situation and that innovation and discoveries that serve the public good have come out of the desire to try new things. Prototyping can help organizations make bold decisions with lower risk, lower investments of time and money, and greater agility.
AI Is New; Prototyping Isn't
Prototyping is a well-established business practice. You develop an idea to address a problem, test it, learn from mistakes and refine it into a viable solution. Today, generative AI can enhance this process with its ability to synthesize data at record speeds.
Prototyping is fundamentally a mindset. It's a way to make intangible ideas visible and concrete. Whether it's a creative concept, a business model or a new framework, prototyping helps clarify big ideas.
As creative professionals and advisors to major companies, we see prototyping in action everywhere. A few examples come to mind: Disney has long created miniature models to explore character and theme park concepts, while designer Yves Béhar experimented with concepts for an electric urban truck to imagine future possibilities. And the R&D innovations of Dyson not only revolutionized household technology and design for consumers, but also the practice of brand marketing itself.
More recently, the C-suite is turning to AI to streamline business planning, reporting and analysis; identify emerging trends; and explore various business scenarios. Generative AI is used to mine vast amounts of real-time data, offering visualizations that provide a strategic overview of potential outcomes.
Consider these hypothetical applications:
- In manufacturing, how might prototyping optimize supply chain management and inventory processes? To what extent could this reduce processing times?
- How can analysis of past performance inform our predictions of next year's customer purchasing behavior? Are we equipped with sufficient production capacity to meet projected demand?
- Could experimenting with different customer service models enhance the customer experience? Would this improve efficiency for our call center team?
- What potential financial outcomes could arise from a merger or the acquisition of a competitor? How long might it take to see a return on investment for shareholders?
According to Statista, the world's top performing companies share a common trait: They invest significantly in R&D — on average, about 15% of sales — which includes prototyping. They also partner with experts and like-minded organizations and pursue joint prototyping initiatives. Modest investments in prototyping can deliver substantial returns.
Whether in the form of 3D models or visual graphs of business scenarios, it's important to remember that prototypes are experiments, not finished products. These prototypes are designed to test multiple ideas quickly, allowing teams to make quick decisions, discard what doesn't work and learn from the experience. It's alright for an experiment to fail because it's a natural part of the testing and learning process. It's also acceptable to “break the rules” during the prototyping process. Embracing questions like “What if we tried this…” is at the heart of innovation.
Data + Human Judgment
The era of leaders relying solely on “gut instincts” has passed. Today, historic and real-time data are essential for sound decision-making, with generative AI as the engine driving data processing. However, AI outputs are not definitive answers. Human judgment is required to review and interpret the outputs and apply them to business decisions. Do these outputs align with the company's mission, core values and ethical standards? Is this a reasonable solution? How do we ensure these innovations align with our strategic goals? How can we use this to create meaningful job opportunities for our employees?
Getting Started
When managed effectively, generative AI combined with the classic practice of prototyping can expand an organization's knowledge base and empower leaders and their teams. We recommend starting with a specific, low-risk business decision and using relevant data to experiment. If you're successful, gradually apply this approach to other business challenges and share your findings with internal stakeholders, shareholders and analysts to foster and nurture a new mindset. These well-managed learning experiences will have a positive lasting impact.