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We may be living in unprecedented times, but today's wave of artificial intelligence actually has historical parallels. The mobile wave that began with the launch of the iPhone in 2007 offers some useful lessons for businesses as they navigate their AI plans.
That's the view of Scott Snyder, a senior fellow at Wharton, adjunct professor at Pennsylvania College of Technology, and chief digital officer at EVERSANA, and Julie Ask, a technology futurist, author, and former vice president and principal analyst at Forrester Research.
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AI, in all its forms, has been criticized for not generating a return on investment, which is all too common, as a recent article in Knowledge at Wharton points out.
“Initially, most enterprise leaders failed to understand the magnitude of the impact that the mobile wave would have on their customers and employees,” Snyder and Ask noted. “They focused solely on 'building apps,' rather than transforming their operating and business models to maximize the mobile opportunity.”
Similarities between AI and mobile
Mobile AI and Generation AI are both disruptive technologies that are changing how people work and think about work. Here's how the current wave of AI resonates with the mobile wave:
Bring Your Own Device. The advent of smartphones gave rise to the “bring your own device” movement, but it was often resisted by organizations concerned about the inadequacy of employees’ personal devices. Either way, smartphones overwhelmed enterprises, and technology leaders introduced tools that users felt would help them work more efficiently. Similarly, ChatGPT and other generative AI tools are voluntary and outside the enterprise, so the use of the technology occurs outside of the guardrails of enterprise technology. Both are examples of the call to “empower employees, or innovation will happen around them,” Snyder and Ask explain. “Enterprises must now embrace ‘bring your own AI’ (BYOAI) with the right controls to enable employees to be productive and innovate using the latest gen AI tools while protecting enterprise data.” Both the mobile AI and gen AI movements represent democratized modes of computing. Mobile devices and their associated app stores, Snyder and Ask point out, “combined intuitive interfaces with powerful computing platforms to captivate users around the world.” “Similarly, the latest AI technology, gen AI, offers unprecedented new capabilities to create content, perform analytics, and enable humans to interact with machines in a more natural way.” Both mobile and AI are built on ecosystems of partners and supporting technologies. The rise of mobile-enabled services has been made possible by “massive, ongoing investments in advanced device technologies, cloud computing, developer platforms, data centers, and cellular networks,” Snyder and Ask note. “So too will gen AI.” Mobile and AI leverage large amounts of data. Finally, both waves of technology are data-intensive, but they work in tandem. “AI companies are creating personal AI devices that help envision a future of virtual assistants and agents that can be accessed through natural language. As with mobile, innovative products and business models will follow,” they explain.
difference
There are differences, too. Snyder and Ask point out that “While mobile computing has seen steady adoption and growth around the world, AI's inherent ability to self-improve, combined with increasing regulatory scrutiny, will set it on a nonlinear and unpredictable trajectory unlike mobile's steady growth.” The impact of AI will differ from mobile because of the following factors:
Mobile relies on hardware, while AI is mostly software for end users. “Consumer adoption of AI will be faster than mobile because consumers don't need to buy new devices,” the co-authors note. “Mobile growth initially relied on consumers upgrading their smartphones every 18-24 months and building networks and infrastructure that gradually improved capabilities. While hardware manufacturers are building next-generation devices with local LLMs, most of the large-scale computing happens in the cloud, so consumers can start with devices they already own.” Because AI operates autonomously, the pace of change is faster than mobile. “Despite the need for resources like GPUs, power, data, and human training, as well as ethical, safety, and regulatory concerns, capabilities are advancing rapidly,” Snyder and Ask write. As AI capabilities evolve, “these tools will begin to generate their own experiences and will no longer depend on human labor. Agents will self-correct and collaborate.” For app newcomers, customer and business acquisition costs will be higher than mobile. “Gen AI will first extend existing services. Think Siri or Microsoft Copilot for employees. For an application to evolve into a true virtual assistant, it will need history and data about individuals. Anticipating needs and providing contextual or personalized experiences will ultimately increase switching costs.” Factors outside the control of LLM manufacturers constrain growth. Large language models face more hurdles than business model and capability constraints in the mobile ecosystem, the co-authors note. “More training data or content is needed to evolve the model. While LLMs can generate synthetic data, the next leap will depend on unavailable content and physical world data.” Additionally, physical limitations of AI include “access to GPUs for training, or the electricity, water, and manpower required to train the model.” There is also the threat of government regulation of AI.
Snyder and Ask advise applying lessons learned from the mobile wave.
Also, AI engineering is the next frontier in technological advancement
“Too many companies are jumping into Gen AI experimentation with little to no thought about how to measure real business impact,” Snyder and Ask warn. “Like mobile, Gen AI will give end users new superpowers that could dramatically change how companies operate and deliver value to their customers. By leveraging lessons learned from the mobile wave, companies will be better prepared for what's to come.”
