We may live in unprecedented times, but there’s actually a historical parallel to today’s artificial intelligence wave. The mobile wave that started with the launch of the iPhone in 2007 provides some lessons that may help enterprises forge ahead with their AI plans.
That’s the view of Scott Snyder, a senior fellow at Wharton, adjunct professor at Penn Engineering, and chief digital officer at EVERSANA; and Julie Ask, a technology futurist, author, and former vice president and principal analyst at Forrester Research.
Also: Linus Torvalds talks AI, Rust adoption, and why the Linux kernel is ‘the only thing that matters’
AI, in all its forms, is criticized for not showing a return on investment, but this all rings familiar, as they point out in a recent article posted at Knowledge at Wharton.
“Early on, most enterprise leaders failed to grasp the magnitude of the mobile wave on their customers and employees,” Snyder and Ask observe. “They focused only on ‘building an app’ rather than transforming their operating and business models to fully unlock the mobile opportunity.”
Similarities between AI and mobile
Both mobile and gen AI are disruptive technologies, changing the way people work and think about their work. Here is how the current AI wave echoes the mobile wave:
- Bring your own X. The arrival of smartphones gave rise to the “Bring your own device” movement, often resisted by organizations concerned about the lack of security employee’s personal devices presented. Smartphones overwhelmed the enterprise anyway, and technology leaders opened up to tools that users felt helped them do their jobs better. Likewise, the self-driven, outside-the-enterprise nature of ChatGPT and other generative AI tools means technology usage arising outside the guardrails of enterprise technology. Both are examples of the call to “empower your employees, or they will innovate around you,” Snyder and Ask illustrate. “Companies must now embrace “Bring your own AI” (BYOAI) with the appropriate controls to allow employees to use the latest gen AI tools for productivity and innovation while protecting enterprise data.”
- Both the mobile and gen AI movements represent democratized modes of computing. Mobile devices and their associated app stores brought “an intuitive interface, coupled with a powerful computing platform, captivated users across the globe,” Snyder and Ask point out. “Likewise, gen AI, the latest AI technology, offers unprecedented new capabilities that can create content, conduct analyses, and allow humans to interact with machines in more natural ways.”
- Both mobile and AI are built on ecosystems of partners and supporting technologies. The rise of mobile-enabled services was made possible through “massive and ongoing investments in advanced device technology, cloud computing, developer platforms, data centers, and cellular networks,” Snyder and Ask point out. “So, too, will gen AI.”
- Mobile and AI leverage data — and lots of it. Finally, both technology waves are data hogs — and are working together. “AI companies are creating personal AI devices that are helping us envision a future of virtual assistants and agents accessed via natural language. Just like with mobile, innovative products and business models will follow,” they illustrate.
The differences
There are differences as well. Snyder and Ask point out. “While mobile computing gained steady adoption and growth across the globe, AI’s innate ability to improve itself, along with growing regulatory scrutiny, will create a non-linear, unpredictable trajectory unlike mobile’s steady rise.” AI’s impact will be different than mobile due to the following factors:
- Mobile depends on hardware, AI is mostly software for end users. “AI consumer adoption will be faster than mobile as consumers don’t need to buy new devices,” the co-authors point out. “Mobile’s growth initially depended on consumers upgrading their smartphones every 18 to 24 months as well as the build-out of progressively more capable networks or infrastructure. While hardware manufacturers are building their next generation of devices with local LLMs, most of the massive computing will be done in the cloud, which means consumers can start with the devices they already own.”
- Pace of change will be even faster than for mobile as AI will operate autonomously. “Capabilities are progressing quickly despite the need for resources like GPUs, power, data, and human training, as well as ethical, safety, and regulatory concerns,” Snyder and Ask state. As AI capabilities evolve, “these tools will start to generate their own experiences and no longer depend on human labor. Agents are beginning to self-correct and work together.”
- Customer and enterprise acquisition costs will be higher than mobile for new app entrants. “Gen AI will initially augment existing services. Think of Siri or Microsoft Copilot for employees. Applications will need history and data about individuals to evolve into true virtual assistants. Anticipating needs and delivering contextual or personalized experiences ultimately increases switching costs.”
- Factors outside of the control of LLM makers will constrain growth. Large language models face more hurdles than the business models or capability constraints of the mobile ecosystem, the co-authors observe. “Advancing models requires more training data or content. While LLMs can generate synthetic data, the next leaps forward depend on content and physical world data that isn’t available.” In addition, physical limitations with AI include “access to GPUs for training or the electricity, water, and human talent required to train the models.” There is also the specter of government regulations on AI.
Embrace the lessons learned in the mobile wave, Snyder and Ask advise.
Also: AI engineering is the next frontier for technological advances
“Too many companies are plunging into gen AI experimentation with little to no sense of how they expect to measure real business impact,” Snyder and Ask caution. “Like mobile, gen AI brings new superpowers to the end-user and has the potential to drastically transform how companies operate and deliver value to customers. Capitalizing on lessons from the mobile wave can only make us more prepared for what’s to come.”
Source: Robotics - zdnet.com
